アンバレラ 2027年会計年度第2四半期 決算説明会サマリー

要点

• アンバレラ（NASDAQ: AMBA）が発表した2027年会計年度第2四半期の売上高は1億810万ドルとなり、エッジAI関連売上高が過去最高を記録したことで、前四半期比7.7%増、前年同期比13.2%増となりました。
• Non-GAAP売上総利益率は59.3%、Non-GAAP純利益は820万ドル（希薄化後1株当たり0.18ドル）に達しました。
• 商用車におけるAI導入が引き続き好調で、自動車向け売上高が過去最高を更新しました。IoT事業も前四半期比で拡大し、消費者向けビジネスを企業向けビジネスが上回りました。
• 経営陣は、IoT分野におけるフィジカルAI需要に牽引され、第3四半期の売上高見通しを1億1500万ドル〜1億2400万ドル（中央値1億1950万ドル）と設定しました。
• アンバレラは、ローリング形式による5年間の獲得可能な最大市場（SAM）予測を2027年度の85億ドルから2032年度には229億ドルへ引き上げました。これは年平均成長率（CAGR）約20%に相当します。最終年度の市場機会のうち、IoTが約70%を占める見込みです。
• メモリの供給と価格設定が引き続き主な不確実要素となっています。経営陣は第3四半期の業績予想については裏付けが得られているものの、顧客が第4四半期の需要に対して十分なメモリを確保できるかどうかについて引き続き評価を行っていると述べました。

主要財務データ

当四半期の売上高のうち、WT Microelectronicsが60.2%、白銅（Hakuto）が11%を占めました。アンバレラは当四半期中に自社株買いを実施しませんでしたが、取締役会は2027年6月30日までに最大5000万ドルの新たな自社株買いプログラムを承認しました。

事業および業績動向

アンバレラは、第2四半期の業績について、5ナノメートルのCV75およびCV72 AI SoCの売上が急速に拡大したことが寄与したと述べています。IoTと自動車向け売上の両方が前四半期比で増加し、自動車向けの伸びがIoTをわずかに上回りました。

同社は、カメラ中心のエッジAI SoCにとどまらず、エッジインフラ分野へ事業を拡大しています。新たなX7 AIアクセラレータはサンプル出荷中であり、アンバレラのSoCやサードパーティ製ARM/x86ホストプロセッサとともにAIコプロセッサとして動作するよう設計されています。経営陣によると、現在のデザインウィンでは消費電力4〜5ワット程度をターゲットとしており、アクセラレータが必要とするメモリ領域も比較的小規模に抑えられています。

アンバレラは、今後5年間の市場予測を引き上げた主な要因として、未発表の追加AI SoCや単体アクセラレータ製品ラインを含む、新しいエッジインフラ製品を挙げています。対象となるアプリケーションには、セキュリティ、小売、宿泊施設、物流、ヘルスケア、ロボティクス、産業用IoTが含まれます。

また、同社は2つの間接販売チャネル・パートナーシップを発表しました。

• CapGeminiは、アンバレラの省電力AIプラットフォームに、エンジニアリング、システム統合、企業向け展開機能を組み合わせます。
• マクニカは、ソフトウェアエコシステムの開発、技術統合、共同市場参入（Go-To-Market）活動など、フィジカルAIおよびエッジインフラ製品を導入支援するための7年間の契約を締結しました。

経営陣によると、各パートナーシップは7年間でそれぞれ約5億ドルの売上機会をもたらす可能性があるとしています。初期のデザインウィンは来年にも売上を生み出す可能性がありますが、アンバレラの全体予測において意味のある売上寄与となるのは2〜3年後と見込んでいます。

アンバレラ初のセミカスタムプロジェクトである2ナノメートルCV8 SoCは、2028年度に初期の量産売上を計上する計画に変更はありません。なお、事業中止となった別の自動車自動運転開発プロジェクトによりGAAP研究開発費が900万ドル減少しましたが、これは以前説明していたセミカスタム案件の1つではないとのことです。

ロボティクス分野における採用機会も拡大しました。経営陣によると、パイプラインは前回の決算説明会で触れた15件のデザインウィンおよび約1億ドルの潜在売上規模を超えて拡大したとのことです。当四半期の新規案件には、CV72を搭載した四足歩行ロボットが含まれています。

経営陣による業績見通し（ガイダンス）

経営陣は、IoT市場からのフィジカルAI需要に牽引され、第3四半期に好調な季節変動を見込んでいます。また、間接販売チャネルの開発が進む中でも、長期的なNon-GAAP売上総利益率ターゲットである59%〜62%を維持しました。

リスクと注視点

メモリ供給は足元での主要な不確実要素です。アンバレラはメモリの仕入れや再販売を行っていないため、メモリ価格の高騰が同社の売上総利益率に直接影響を与えることはないと経営陣は述べています。しかし、供給の不調やシステムコストの増加により顧客の生産量が減少し、結果としてアンバレラ製チップへの注文が減少する可能性があります。

経営陣は、第2四半期実績および第3四半期見通しにおけるメモリ市況の売上への影響は僅少であると報告しました。ただし、メモリの入手可能性や回避策について顧客との議論を進めている段階であり、第4四半期の見通しは不確実性が残っています。

ベンダーがAIデータセンター需要を優先していることから、サプライチェーン全体でのコストも上昇しています。アンバレラは、長期的な売上総利益率目標を維持するため、関連するコスト上昇分を顧客に転嫁する方針です。

新たなエッジインフラ市場は競争が激化しています。経営陣は競合他社としてエヌビディア、クアルコム、多数のスタートアップ企業を挙げる一方、アンバレラの省電力性能および確立されたハードウェア・ソフトウェアプラットフォームを主要な差別化要因として強調しました。

アナリスト質疑応答のハイライト

• X7の位置付け：このアクセラレータは、アンバレラ自社製SoCまたはサードパーティ製ARM/x86プロセッサとバンドル可能です。顧客は基板の設計変更を必ずしも行うことなく、AI性能を追加できます。
• チャネルの拡大：マクニカは小規模〜中規模の小口案件を取りまとめ、CapGeminiはより大規模で複雑な企業向け展開に注力することが期待されています。マクニカはすでにドローン、小売、製造分野での案件を捉えています。
• ロボティクス・アーキテクチャ：経営陣は、ロボティクス分野が自動運転車と同様の統合プロセスを辿ると予想しています。現在の大半の顧客は知覚（パーセプション）ソリューションを求めていますが、長期的なロードマップではドメインコントローラ・アーキテクチャの採用が急増しています。
• セミカスタムモデル：顧客が製品仕様を提供し、アンバレラはAIアクセラレータ、NPU、ISP、エンコーダ、CPUなどの自社IP（知的財産）を再利用します。また同社は、開発されたチップを競合しない他の顧客へ販売する権利を保持することも目指しています。
• 売上総利益率への影響：経営陣は現在、CapGeminiおよびマクニカとのパートナーシップによって、長期的なNon-GAAP売上総利益率目標（59%〜62%）が変更されることはないと見込んでいます。

決算説明会文字起こし全文

決算説明会の完全なトランスクリプト

経営陣による説明

Operator

Thank you for standing by, and welcome to the Ambarella's Second Quarter Fiscal Year 2027 Earnings Call. [Operator Instructions] As a reminder, today's program is being recorded.

And now I'd like to introduce your host for today's program, Louis Gerhardy, Vice President, Corporate Development. Please go ahead, sir.

Louis Gerhardy

Thank you, Jonathan, and good afternoon. Thank you for joining our second quarter fiscal year 2027 financial results conference call. On the call with me today is Dr. Fermi Wang, President and CEO; and John Young, CFO.

The primary purpose of today's call is to provide you with information regarding the results for our second quarter of fiscal year 2027. The discussion today and the responses to your questions will contain forward-looking statements regarding our projected financial results, financial prospects, market growth and demand for our solutions, among other things.

These statements are based on currently available information and subject to risks, uncertainties and assumptions. Should any of these risks or uncertainties materialize or should our assumptions prove to be incorrect, our actual results could differ materially from these forward-looking statements. We're under no obligation to update these statements. These risks, uncertainties and assumptions as well as other information on potential risk factors that could affect our financial results are more fully described in the documents we file with the SEC.

Access to our second quarter fiscal year 2027 results press release, transcripts, historical results, SEC filings and a replay of today's call can be found on the Investor Relations page of our website. The content of today's call as well as the materials posted on our website are Ambarella's property and cannot be reproduced or transcribed without our prior written consent.

Before starting the call, we hope to see you at one of the following investor events that we have scheduled in our third quarter. First, on September 8, we'll host a DNB Bus Tour at our offices in Santa Clara. September 9, we'll be at Citi's 2026 Global TMT Conference in New York. September 15, we'll participate in Piper Sandler's Growth Frontiers Conference in Nashville. September 16, we will host Sanford Bernstein's 8th Annual West Coast Semiconductor Bus Tour. And during the week of October 4, we will have a European NDR with cities to be determined. Also available to investors during the third fiscal quarter will be our booth and presentations at the AI Infrastructure Summit in Santa Clara on September 15 to 17. We hope to see you there where we will lead the Physical AI track with a number of edge AI and robotics demos in our exhibit area.

Fermi is now going to provide a business update for the quarter. John will review the financial results and outlook, and then the 3 of us are available for your questions.

Fermi?

Fermi Wang

Thank you, Louis, and good afternoon. Thank you for joining our call today. Driven by a new record level of edge AI revenue, we reported fiscal Q2 revenue slightly above the midpoint of our guidance with non-GAAP EPS of $0.18, and with guidance for seasonal fiscal Q3. By product, we are in the midst of a very steep revenue ramp with our 5-nanometer CV75 and CV72 AI SoCs. And by market, we had sequential growth in both IoT and Auto with automotive revenue driven by commercial vehicles.

The market is increasingly recognizing the strategic value of edge AI as well as our edge AI and Physical AI platform leadership. We continue to make significant progress with the expansion of our edge AI platform leadership, including new go-to-market strategies and engineering and market development for a number of new higher-value AI SoCs, some of which extend our reach into entirely new markets. We remain optimistic about the long-term secular growth opportunities in the edge AI market and our R&D priorities are aligned with both the Physical AI markets that represent a vast majority of our total revenue today as well as the robotic and edge infrastructure markets that are in the early stages of developing.

Altogether, our technology, product and new go-to-market combined with the significant secular growth in edge AI are increasing our 5-year serviceable market forecast today. Before I review our new market forecast, I would like to step back and discuss the market environment we are in. Demand signals for the application of edge AI remains strong. At the same time, it is obvious that memory vendors and the entire supply chains are prioritizing AI data center demand, which is resulting in rising supply chain costs for everyone. Surging memory price and the scarcity of supply are impacting the entire industry.

Related to this, we are providing significant assistance to customers who are attempting to create a wide variety of workarounds to the memory situation. Ambarella itself is also facing rising supply chain costs, and we plan to pass this cost to our customers to maintain our long-term gross margin target of 59% to 62%. Returning to our rolling 5-year serviceable market update, I would like to remind you of our methodology. Our SAM for any given year is based on the products we expect to have available for production in that year, overlaid on the total available market projections from a number of third-party research firms.

So our 5-year SAM captures any revenue-generating products announced or unannounced on our road map in the next 5 years. Our prior 5-year rolling SAM was announced in May 2025 and projected a 5-year fiscal year '26 to fiscal year '31 compounded annual growth rate of about 18%, with Auto representing a slightly higher proportion of the terminal year. Our new 5-year rolling SAM from $8.5 billion in fiscal year 2027 to $22.9 billion in fiscal year '32 represents a CAGR of about 20% with IoT markets now representing about 70% of the terminal year.

While there are several factors behind the strong growth and the underlying mix change, I will focus on the most important change. In the last year, it has become clear that operational efficiency or the ability of our enterprise to generate more revenue and/or to reduce expenses is likely to be a key driver of our emerging edge infrastructure business. Operational efficiency at the edge refer to the use of open weight and distilled models running on on-premise inferencing hardware in contrast to the large frontier models that run in the cloud. Benefits of this approach include reduced latency, data protection, privacy, lower bandwidth costs and high reliability.

Target markets include security, retail, lodging, logistics, healthcare and more. The on-premise operational efficiency use case has emerged with growing expectations for sustainable high-volume inferencing and increasingly for agentic AI and Physical AI application that can perceive, reason and ultimately act in the physical world. The key question has become who can help the enterprise lower the cost per useful AI inferencing outcome? This is where Ambarella's superior performance per watt portfolio kicks in, providing the efficient edge intelligence needed to enable this next-generation agentic and Physical AI workload at scale.

With this perspective, in the last year, we have several new products in development targeting on-premise hardware or what is commonly called edge infrastructure. As you know, we already have our N1-655 AI SoC in the market, and we have additional unannounced AI SoCs in development. We also are implementing a stand-alone AI accelerator product line targeting the edge infrastructure market. Together, this new edge infrastructure products, both AI SoCs and stand-alone AI accelerators represent the single most important reason for the upward revisions in our SAM.

Before I introduce our first stand-alone AI accelerator, allow me to be clear about our terminology. We define edge AI SoC as one integrating all of the accelerated computing functions into a single chip, camera perception, AI accelerators, CPUs, encoding and so on. We define an AI accelerator as an AI processor that is not camera specific and targets a wide variety of digital or physical modalities. We believe this type of multi-modality is critical for edge infrastructure applications that target operational efficiency.

While not formally announced, I would like to preview one of the new AI accelerators that will anchor this new product category for us with another well-defined, well-performed product already behind it. We refer to this new AI accelerator as X7. This SoC is sampling now and expected to land initial design wins in edge infrastructure applications where it can serve as an AI coprocessor for host processors such as ARM or x86. Together with our new product thrust, expanded market reach and the SAM, we expect our revenue growth to be supported with 2 incremental go-to-market strategies. First is the multistep establishment of indirect sales channel and the second is a semi-custom chip strategy, both of which will augment our existing direct sales efforts.

As a reminder, virtually all our revenue is generated by our direct sales teams. And today, I'm excited to announce 2 material partnership agreements to develop our indirect sales channel. Combined, these 2 partnership plan to drive a significant amount of incremental revenue over the next 7 years through customers who have largely been unserved by us so far. First, today, we announced Ambarella's strategy partner with CapGemini designed to help enterprise adopt edge AI and Physical AI solution faster by reducing the complexity of moving from evaluation to scalable deployment.

By combining Ambarella's power-efficient AI software and platforms with CapGemini's global engineering, system integration and industry expertise, the partnership aims to help customers improve operational efficiency, enhance real-time decision-making and deploy intelligent system and in physical world environment with greater speed, scalability and confidence.

In our second partnership to develop our indirect channel, today, we also announced a 7-year agreement with Macnica, a leading global technical distributor. Macnica will support both Ambarella's Physical AI and the new edge infrastructure products by developing and supporting an independent software vendor ecosystem, including onboarding, technical integration support and joint go-to-market progress. With this ecosystem in place, Ambarella solution can be offered as individual component or as a complete bundle for multiple edge AI vertical markets, including video analytics, smart city, edge computing platforms, robotics, industrial IoT, intelligent transportation systems, retail analytics, security and surveillance.

I want to emphasize the importance of the indirect channel to serve small and midsized customers and highly fragmented market like robotics. However, the indirect channel is also critical to support our more complex AI SoC targeting the edge infrastructure where a broad network of partners is vital for our long-term success. Meaningful revenue is expected in 2 to 3 years and will grow as we introduce new products for the market. Our second incremental go-to-market is our semi-custom opportunity, which can enable us to gain more share in existing market and reach into new markets.

We have our first semi-custom project underway, the 2-nanometer CV8 SoC, which is expected to generate first production revenue in fiscal 2028. And we are in discussion with other companies for additional semi-custom chip projects. Our representative customer engagement this quarter once again demonstrates Ambarella's expanding traction across a broad set of applications, robotics, automotive, security, trail cameras and smart video intercoms. With a CV72-based quadruped robot validates Ambarella's high resolution, high multi-camera edge AI capabilities in robotics. A major S&P 100 communication equipment company announced an AI-based enterprise video intercom, further extending our reach in the emerging access control market.

We landed another win with Moultrie for AI trail cameras and win with Canon, Suprema, IDS and Sepro further strengthen our AI monitoring pipeline with CV75, CV72, CV5 wins using our own AI ISP software. Through Tier 1s, we had 2 in-cabin vehicle wins with Tier 1s in China, one for driver monitors and one -- the other for more complex camera monitor system used in Audi and the VW vehicles. The breadth of these wins and the wide variety of corresponding AI workloads highlight the programmability and the flexibility in both our AI SoCs and our Cooper Developer Platform. This ease of use is facilitating the onboarding and expansion of our indirect sales channels. Very few competitors can offer this type of proven platform with more than 50 million edge AI SoCs shipped.

In conclusion, I remain very excited about the overall growth opportunity of the edge AI market and our company-specific growth drivers put us in a unique position to benefit. Ambarella is expanding beyond low-power AI SoC to deliver the complete foundation for Physical AI, and we are becoming a full stack Physical AI platform provider.

With that, I will now turn it to John.

John Young

Thank you, Fermi. I'll now review the financial highlights for the second quarter fiscal year 2027 ending July 31, 2026. I will also provide a financial outlook for our third quarter of fiscal year 2027 ending October 31, 2026. I'll be discussing non-GAAP results and ask that you refer to today's press release for a detailed reconciliation of GAAP to non-GAAP results.

For non-GAAP reporting, we have eliminated stock-based compensation and acquisition-related expenses adjusted for the impact of taxes. In addition, this quarter, as described in our Q1 fiscal 2027 10-Q filing as a subsequent event, we recognized a $9 million reduction in our GAAP research and development expense due to the cancellation of a customer's development project. We do not expect any impact on our non-GAAP outlook from this development. For fiscal Q2, revenue was $108.1 million, slightly above the midpoint of our prior guidance range of $105 million to $111 million, up 7.7% from the prior quarter and up 13.2% year-over-year.

Automotive revenue established a new revenue record on continued strength as the commercial vehicle adoption of AI remains strong, and Auto revenue slightly outpaced the growth in our IoT business, where our enterprise-driven businesses outperformed our consumer-led businesses. Non-GAAP gross margin for fiscal Q2 was 59.3%, below the midpoint of our prior guidance range of 59% to 60.5%. Non-GAAP operating expense in Q2 was $57.4 million, slightly below the midpoint of our prior guidance range of $56 million to $59. Q2 net interest and other income was $1.8 million. Q2 non-GAAP tax provision was approximately $344,000. We reported Q2 non-GAAP net profit of $8.2 million or $0.18 per diluted share.

Now I'll turn to our balance sheet and cash flow. Fiscal Q2 cash and marketable securities were $272.3 million, decreasing $5.5 million from the prior quarter, but increasing $11.1 million from the same quarter a year ago. The sequential decrease in cash and marketable securities was primarily due to higher payments for IP licenses. Receivables days sales outstanding decreased from 35 to 32 days. While inventory dollars declined 4% sequentially, the days of inventory increased from 145 days to 157 days. Operating cash outflow was $260,000 for the quarter.

Capital expenditures for tangible and intangible assets were $6.8 million for the quarter. Free cash outflow was $7.1 million for the quarter. During the second quarter of fiscal year 2027, we did not repurchase shares of our stock. During the second fiscal quarter, Ambarella's Board of Directors authorized a new $50 million repurchase program valid through June 30, 2027. The repurchase program does not obligate the company to acquire any particular amount of ordinary shares and it may be suspended at any time at the company's discretion. WT Microelectronics, a logistics partner in Taiwan that ships to multiple customers in Asia, was 60.2% of revenue for the second quarter. Hakuto, a logistics and distribution partner in Japan, was 11% of revenue in the quarter.

I'll now discuss the outlook for the third quarter of fiscal year 2027. We are anticipating favorable seasonality in our fiscal third quarter with revenue in the range of $115 million to $124 million or $119.5 million at the midpoint. At the midpoint, we expect our growth to be led by Physical AI demand from the IoT market. We expect fiscal Q3 non-GAAP gross margin to be in the range of 59% to 60%. We expect non-GAAP OpEx in the third quarter to be in the range of $56.5 million to $59.5 million. We estimate net interest and other income to be approximately $1.9 million, our non-GAAP tax expense to be approximately $700,000. And our diluted share count is expected to be approximately 44.9 million shares.

Thank you for joining our call today. And with that, I'll turn the call over to the operator for questions.

Operator

[Operator Instructions] And our first question for today comes from the line of Christopher Rolland from Susquehanna.

質疑応答

Dylan Ollivier

This is Dylan Ollivier on for Christopher Rolland. So it's nice to see your road map sort of expanding, and I know that you announced this X7 accelerator. I was hoping to hear a little bit more about this new chip. Is this a chip that you can bundle with your existing N1 portfolio? Or does this address a different part of the stack?

Fermi Wang

So yes, Chris, for the X7, this is -- chip is an accelerator, which can be bundled with any host, including our own chip. So in fact, that some of our customers using a certain part number and when they feel they need to have more AI performance for certain workloads, the X7 give them a flexibility to upgrade the product without redesign the board. So this accelerator definitely is a way to design that. But in addition to supporting our own SoCs, but any other CPU like ARM or Intel chip, Intel CPUs that we can also bundle X7 with that as an AI accelerator.

Dylan Ollivier

Great. I appreciate this. And for my second question, I wanted to ask about sort of the Physical AI and humanoid opportunity. Is this responsible at all for this increase in SAM? Are there any new engagements or new designs that you can point us to?

Fermi Wang

Yes. So definitely, that's a big part of that. And last -- in the last earnings call, we talked about 15 design wins for the robots, including for roughly $100 million. Although we didn't give you another breakdown, but I can say that we add more design wins to that pipeline and a higher revenue target. So from that point of view, we continue to make progress. But in addition to robots, I also think that edge infrastructure and also enterprise security as well as portable video are all the reasons that we are increasing our SAM number.

Louis Gerhardy

Yes, Dylan, we did -- Fermi mentioned a quadruped robotic dog with the CV72 chip this quarter. So continue to add on to the robotics wins we've described before.

Operator

And our next question comes from the line of Joe Moore from Morgan Stanley.

Joseph Moore

I wonder, first, in terms of the broader ecosystem, you talked about some of the challenges in memory. What is that meaning for your business? Do you think -- is there a risk of pull forwards or things like that because people are trying to get ahead of memory price increases? Is there pressure on you? Just what are you seeing from that memory impact from your customers?

Fermi Wang

Right. So we continue to monitor this situation very closely, by talking to customers all the time. So for Q3, we are comfortable with our -- the guidance we provide today. In Q4, we continue to talk to customers to make sure our customer will have -- we can secure enough memory for a Q4 business. That's definitely the uncertainty that we are dealing with.

Joseph Moore

Okay. That's helpful. And then in terms of opening up to a broader ecosystem, distribution partners, things like that, I think you made the comment about -- that would take a couple of years to inflect. I guess I would sort of think that those customers would act a lot more quickly and would -- that pipeline could build a lot more quickly than what you had seen previously in automotive. Just what do you -- what was the comment that I maybe misunderstand there? And then what is the time line to start to see traction from that kind of broader ecosystem?

Fermi Wang

Right. So when I say 2 to 3 years, we talk about meaningful revenues. And I agree with you that we -- in fact, we already start seeing a small amount of design wins, which can generate revenue next year. But when we talk about meaningful revenue that will have an impact to our revenue forecast, I think that will take 2 to 3 years. In fact, when we talk to both CapGemini and Macnica, we kind of -- in fact, the range of revenue we are expecting from this collaboration is $0.5 billion with each one of them. So from that point of view, we're definitely looking forward to gradually ramp up the revenue for the next couple of years and start seeing meaningful revenue behind that.

Operator

And our next question comes from the line of Tore Svanberg from Stifel.

Tore Svanberg

Congratulations on the Macnica and CapGemini partnerships. I'm curious on those for me. What are some of the early use cases that those 2 partners are going to be helping you with? Maybe you can call it some markets or applications. And how should I think about that in the context of your Cooper platform? Are they going to be working with you on Cooper? Are they going to be providing some of their own software? Just curious how that's going to play out?

Fermi Wang

Right. So -- let me answer the second question first. Yes, both of them will use Cooper. In fact, that's a key driver for them to select to work with us because they see a very mature software platform they can immediately tackle on and start building around it, generating infrastructure for their own product line. So that our mature AI SoC as well as a mature Cooper software platform is the probably most critical engineering aspect that we offer to our partners.

Go back to the potential market that we are talking about. In fact, there are multiple of them. And in fact, when I talk to Macnica, CEO in that meeting, they are highlighting that they have already started winning design wins with our solution on drones, on retail channels and also manufacturing. So that is definitely -- you can see that it's really a large market that. But -- however, most of the design win is small and segmented at beginning, but can ramp up to -- if they can ramp up to large volume of business, that will take time. But we already start seeing our partners start talking about different applications.

Louis Gerhardy

Tore, it's Louis. They can work together as well. As Fermi said, Macnica can serve small to midsized markets that oftentimes are very fragmented. But really for CapGemini, it's large enterprise customers, and you can look at who they've talked about before. Those are the type of customers we'd really go after with them. So they're very complementary to each other.

Tore Svanberg

Very good. And as my follow-up, on the edge infrastructure market, this is obviously a completely new area. It sounds like that's the sort of biggest contributor to your increased SAM. I'm just curious, who's going to be some of your partners there? I mean, are these going to be your end customers sort of building their own infrastructure? Or is there going to be like an intermediary company that's building it? Is it going to be the traditional server guys? Yes, just curious how that's all going to play out?

Fermi Wang

Well, I think, obviously, we're going to continue to talk to some of the large customers directly. But at the same time, we're counting on CapGemini and Macnica help us to penetrate this because they're already in that market, they're already selling solution to the existing edge AI customer with their existing solution. So working with them will help us to ramp up our revenue much faster than just were talking to direct customer directly.

Operator

And our next question comes from the line of Quinn Bolton from Needham & Company.

Quinn Bolton

I just wanted to ask just longer term on the -- sorry, Macnica and CapGemini partnerships. Does that change the long-term gross margin target? I assume that there's probably some allocation of revenue that would be attributed to those partners. And so I'm wondering if that has any gross margin implications as that indirect channel ramps?

Fermi Wang

Right. So today, I think our long-term gross margin is still 59% to 62%. We are definitely trying to continue to watch because this is just -- we just start ramping up this business. If there's any change, we'll definitely inform our investors. But today, for us, after we talk to CapGemini and Macnica, we don't feel there's any need to change that target today.

Quinn Bolton

Got it. And then I guess just a clarification on the $9 million charge for the project that was canceled. Was that a semi-custom project that was canceled? And does that have any impact on your expected revenue time line for the semi-custom business?

John Young

Yes. Thanks, Quinn. It is not one of the semi-custom opportunities that we were -- that we've talked about. It was a development project with I guess, you could say, an automotive customer, auto autonomy customer. And we've been negotiating the termination of that for quite some time. And in Q2, we finalized the agreement.

Operator

And our next question comes from the line of Kevin Cassidy from Rosenblatt Securities.

Kevin Cassidy

Going back to the shortage on the memory side and you've got near-term visibility. But I'm wondering on the designs, I know a lot of your customers or the market out there is probably dominated by a GPU-based embedded product that uses much more DRAM than yours would. Are you seeing any additional interest because you're more efficient with DRAM content?

Fermi Wang

Well, yes, first of all, the memory situation is dire for everybody, but some of our competitor who has more money to buy more memories. But however, any customer who come to us for the edge AI or Physical AI, they probably only use GPU for their first-generation product, and they understand. So the memory cost is just one reason, but more importantly, it is power efficiency and other reason. But the memory cost definitely is a driver for people start considering what's the more efficient way to do the product. So I agree with you that some of the -- most -- in fact, almost all the customers who come to talk to us is because our power efficiency solution and the lower cost solution than what they're using.

Kevin Cassidy

Okay. And maybe along the same lines with the AI accelerator, you'd be competing against a GPU that uses a lot of memory also. What is the memory architecture inside your X7?

Fermi Wang

Well, in fact, that we need a much smaller footprint. For example, we only need 4 megabytes memory for the accelerator running large language model. So just give -- show you the -- and more importantly, the accelerator, the power envelope you have to fit in is anywhere between 4 to 5 watts in the current design win. So all of the power efficiency, memory size and also cost is really helping us to penetrate this market right now.

Operator

And our next question comes from the line of Suji Desilva from ROTH Capital.

Sujeeva De Silva

just a clarification for me on the X7 chip. Is that competing really only with edge GPUs? Or is it other AI specialty chips? Or how should we think about the competitive landscape for this new offering?

Fermi Wang

Right now, well, in addition to NVIDIA and Qualcomm having similar products in this market space, there are probably 50 start-up companies doing similar chips. So it's a crowded space. But however, at the end, it's really about the power efficiency because I just talked about to run a certain workload, you have to have a mature -- not only a power-efficient solution, but mature hardware and software, which I think we are one of the very few that can do that today.

Sujeeva De Silva

Okay. That's helpful for me. And then my other question is you're talking about customization now projects. I'm just wondering what's precipitated the demand from the customers or your push to provide customization? What's newer versus your standard product history now that's driving the need for that or your desire to do that?

Fermi Wang

I think you're talking about the optimization for the memory situation. Is that correct?

Sujeeva De Silva

Well, I think...

Louis Gerhardy

Semi-custom.

Fermi Wang

Oh, semi-custom.

Sujeeva De Silva

Semi-custom, I apologize. Yes.

Fermi Wang

So -- yes. For semi-custom customers, in fact, we basically allow our customers give us a spec and we build on the spec. But however, when we negotiate spec with a customer, we need to make sure that we can sell the spec to somebody else. So we -- for the semi-custom chip, we pretty much build a purpose chip for the one customer, which they benefit from this. But at the same time, we can sell the chip to others that are not competing with the key customer. That's the business model and how it works on the engineering side.

Louis Gerhardy

But of course -- Suji, it's Louis. We'll try to offer as much of our own IP in those semi-custom chips as possible. For example, we have our own IP for the AI accelerator, the NPU for all the perception capabilities, including ISP, and the encoder, the CPUs, all of those functional blocks are available for a customer to develop a semi-custom or custom chip with.

Operator

And our next question comes from the line of Liam Pharr from BofA.

Liam Pharr

Is there a way to frame how much memory cost inflation you're absorbing this quarter, either in basis points or maybe what gross margin would have been without any memory cost inflation? And is the path back above 60% feasible while memory prices stay elevated? Or does that require pricing to come down?

Fermi Wang

Right. So first of all, the memory price doesn't impact our gross margin. It really only have a potential to impact how many chips our customers can buy. So memory cost because we don't buy memory, and we don't resell memory. So the memory price has no impact to our gross margin. So that -- I think I hope that answers your question. But the real question for us is how that memory cost can -- because our customers need to increase the price, whether that will reduce the total volume they can sell and therefore, reduce the total ordering to us, that's something we need to continue to observe. In Q2 and Q3, we see a little impact on our revenue because of memory situation. We continue to watch for the Q4.

Liam Pharr

And then I guess for my follow-up, Q3 is guided up 10.5% roughly sequential versus 13.5% last year. How much of this next quarter is normal seasonality versus underlying end demand strength? And given you flagged Q4 memory supply, obviously changing the demand picture, how should we think about Q4 seasonality and whether the full year 10% to 15% is still reasonable for the full -- for the guide?

Fermi Wang

Right. So I think the outcome this year is still a little uncertain because of the memory constraint that you talk about. And like I said, we continue to talk to our customer for that to monitor how that impacts our performance in Q4. Barring for any memory impact to our revenue, I think that you should expect Q4 was a regular seasonality.

Operator

And our next question comes from the line of Gus Richard from Northland.

Auguste Richard

Robotics architecture look an awful lot like an autonomous car in terms of what it needs to do. And I'm just wondering, you have a domain controller for autos and you have the CV products. Are you seeing any traction in the domain controllers? And -- and then any clarification on where you're seeing the strength? Is some of this coming out of China?

Fermi Wang

Right. First of all, you're 100% right that a lot of robot design system architecture looks just like autonomous driving car, which I totally agree. And however, I think the robotic market situation really reminds me autonomous driving 7 years ago when that at that time, all of our automotive customers in trying to just using individual modules and put the solution together and start demoing and selling the first generation product. I think this is how we are at with the current robots. We see a lot of customers are rushing out their first-generation product by putting individual components together to demo their capabilities.

However, we do believe that integration path of the robotic will be very similar to what happened to the autonomous driving car. It is there will be people going to buy perception system, but down the road, people want to buy domain controller. We do see both opportunity today, but I would say majority of our customers today is asking for perception modules, perception solution. But on their road map, they want to have a way that can buy a domain controller in the long run. So I think we have a complete road map. We can sell just perception system to a customer today. In fact, people want to buy brain -- domain controller like for the brain of the robots, we have the solution, too. But our plan is we're going to continue to develop solution for both so that we can cover the total space of robotics.

Auguste Richard

Got it. And then just if I think about, again, robots, cars are 2D and robots are 3D. And I'm just wondering, is one of the limitations of penetration training and can you help your customers train robots. Thinking about humanoid, but -- go ahead, sorry.

Fermi Wang

Right. So in terms of training, it's really about how to collect data. One thing we help our customers is we build a platform for people to collect data easily. And also we provide a platform that can provide a service to help people to label those data automatically. So people can use our system to -- reference design to collect data. In fact, some of the, I would say, the people doing mapping, generating the 3D mapping are using our system to collect data. And also, we are providing service to some of our automotive customers that we can -- using our tools to auto labeling all of the data they generate. Those are 2 things we can help to provide assistance on the training side.

Operator

And our final question for today comes from the line of Martin Yang from Opp & Co.

Martin Yang

Fermi, you sized the potential revenue from CapGemini and Macnica pretty similarly, but they face different variety of customers. Can you maybe talk about the methodology you arrived at those dollar figures? Is a similar methodology or a very different approach to size those potential markets?

Fermi Wang

Yes. Go ahead.

Muneyb Minhazuddin

This is Muneyb just jumping in there. I think both Fermi and Louis were commenting earlier about how complementary they were, right? So I think, one, on the Macnica side, I think Louis has commented, it was is large-scale, medium, large kind of customers we haven't addressed in the past. So think of them as a large volume play where we've typically directly engaged with high-volume customers. These will start aggregating a whole bunch of small, midsized customers that we did not have access to in the past. So it's a volume play, and I think Fermi already indicated that we're starting to see some small design wins come through with these distribution.

And then if you think about CapGemini, it's more of a value play. And I think Louis indicated before, these are large enterprises and customers who will bring complex solutions, deploy at scale to enterprises. So the modeling is on both slightly different. One, distribution channels, reseller scaling with small design wins, so building up small volume. The other ones are large customers and logos, which have much larger opportunity deals, but complex opportunities. So on both sides, the modeling is done on value versus volume. And I think the earlier question was also, you should see different time lines on this.

So we do expect faster time lines on the distribution side and more longer time lines on the more larger complex opportunities. But the modeling has been built out over 7 years of how this will come to fruition. And of course, they are -- some of them new to our products. So initial ramp-up, market making, pilot opportunities is what we are allowing for. But we will keep you updated as we start winning some large deals and meaningful revenue, as Fermi pointed out, in future quarters.

Martin Yang

Great. I have a follow-up on X7. Is that accelerator chip primarily targeted for as a channel product? Or there's no distinction between for channel or for direct?

Fermi Wang

There's no distinguish. And in fact, that I'm expecting that both CapGemini and Macnica will do product rapid design for that and targeting different customers.

Operator

This does conclude the question-and-answer session of today's program. I'd like to hand the program back to Dr. Fermi Wang for any further remarks.

Fermi Wang

And thank all of you for joining our call today, and I hope to see you and talk to you next time.

Operator

Thank you, ladies and gentlemen, for your participation in today's conference. This does conclude the program. You may now disconnect. Good day.