Semi Doped

Vikram Sekar and Austin Lyons
  • 5.0 (22)
  • Technology

The business and technology of semiconductors. Alpha for engineers and investors alike.

  1. 3d ago

    AI Inference Is a Data Movement Problem: Microsoft's Saurabh Dighe on Maia 300

    Microsoft's Maia 300 is a fully co-designed system, optimized to solve the memory-bound "data movement problem" of emerging agentic AI workloads.  Saurabh Dighe, Corporate VP of Azure Hardware Systems at Microsoft, sits down with Austin to detail the architecture, from its TSMC N2P chiplet design enabling 18 TB/s of HBM4 memory bandwidth to its use of AI agents for automated kernel optimization.  They discuss how this full-stack approach aims to maximize intelligence per watt and per dollar for Microsoft's planet-scale services. This episode is presented by @CrusoeAI. Crusoe's Serverless Fine-Tuning trains an open model like Llama, DeepSeek, or GLM on your own data, and you pay per million tokens, not per GPU hour. About $2 buys roughly 5M training tokens on smaller models. Get $5 in credit to start: https://semidoped.com/crusoe Key Takeaways: - Microsoft views AI inference as a data movement problem, not a compute problem. The energy cost of moving data to feed the compute engine can be two orders of magnitude higher than the compute itself. - The design of Maia 300 is driven by the shift from chat to agentic AI, which creates memory-bound workloads. The system delivers a 7x token throughput increase per rack over Maia 200, with a 5x increase in rack-level memory bandwidth. - Maia 300 uses a chiplet architecture to "extend the shoreline" of its single TSMC N2P compute die. This allows it to accommodate eight HBM4 stacks, delivering 18 TB/s of memory bandwidth. - Microsoft is using AI agents for "agentic kernel optimization" to automate the creation of low-level software. This strategy aims to bypass the manual optimization bottleneck and enable "Day-zero model availability" for new AI models. - The system is built for deployment flexibility. While servers use direct liquid cooling, a rack-level "heat exchanger unit" allows the 167 kW racks to be deployed in traditional air-cooled data centers. - The network is a two-tiered scale-up fabric built on standard Ethernet. Copper connects the 96 accelerators within a rack, while optics connect racks into superpods of up to 12,288 accelerators. Chapters: 0:00 Intro: Saurabh Dighe 1:39 Microsoft's Silicon Philosophy 5:52 Workload Shift: Agentic AI 9:32 The Data Movement Problem 14:20 Crusoe Ad Read 17:47 Maia 300 Specs 24:07 Chiplets & HBM4 29:12 Prefill/Decode Disaggregation 30:33 Two-Tiered Network 35:45 NPO vs. CPO 36:46 Rack-Scale Power & Cooling 41:35 Agentic Kernel Optimization 49:08 What's Next: Maia 400 Follow Chipstrat: Newsletter: https://www.chipstrat.com X: https://x.com/chipstrat Follow Vik: Newsletter: https://www.viksnewsletter.com/ X: https://x.com/vikramskr Follow Semi Doped: Get more of Austin and Vik daily, free: https://daily.semidoped.com/

  2. Oct 8

    Synopsys’s Amazon Deal and GPT-Synopsys: Ravi Subramanian

    The demand for workload-specific hardware is forcing a strategic shift in the semiconductor IP business. Synopsys's Ravi Subramanian joins Austin to discuss why the company is moving to a new royalty-based model for its custom "application optimized IP," starting with a billion-dollar deal with Amazon. Ravi also unpacks Synopsys's partnership with OpenAI and its vision for AI "agent engineers" that augment human designers, while explaining why physics-based simulation remains the ultimate ground truth. This episode is presented by Crusoe. Try Crusoe's managed inference for open models with $5 in free credits: https://semidoped.com/crusoe "in physics we trust is because at the end of the day, the ground truth to have the confidence, not hallucinate and be deterministic in your outcome is really what defines is a product going to be successful or not." — Ravi Subramanian, Synopsys Key Takeaways: - The hyperscaler IP model is shifting. Because 1-2 applications drive most revenue, companies like Amazon now demand custom “application optimized IP” instead of standards-based IP. - Synopsys is adopting a new business model for this custom work: a license fee, a “customization fee” to prioritize scarce engineers, and for the first time, a royalty on the final chip. - This new royalty model mirrors the Arm-Amazon partnership from a decade ago, aligning Synopsys with its customer’s 12-month design cycles and expanding its role to system-level bring-up. - Synopsys’s AI strategy isn't just tools, but a new consumption model: customers can “hire” an AI agent engineer with a specific skillset, fitting directly into existing budgets and team structures. - The GPT-Synopsys partnership with OpenAI is a turnkey service bundling a specialized LLM, the necessary compute, and forward-deployed engineers to solve the key adoption hurdles of model quality and compute scarcity. - AI is for exploration, but physics is for verification. Accurately modeling a modern CFET requires 200x more device equations than a planar FET, a complexity that demands physics-based simulators as the ground truth. Chapters: 0:00 Intro: The Amazon & OpenAI Deals 2:39 Application Optimized IP 6:04 The New Royalty Business Model 12:14 How Standard IP Differentiates 18:20 The Arm-Amazon Analogy 22:00 Synopsys's Role in Test Chips 26:06 The Three Stages of Chip Design 31:27 The 'Agent Engineer' AI Model 36:20 The GPT-Synopsys Partnership 39:27 In AI We Believe, In Physics We Trust Follow Chipstrat: Newsletter: https://www.chipstrat.com X: https://x.com/chipstrat Follow Vik: Newsletter: https://www.viksnewsletter.com/ X: https://x.com/vikramskr Follow Semi Doped: Get more of Austin and Vik daily, free: https://daily.semidoped.com/

  3. Oct 1

    Every Muse User Gets 2 CPUs and 8GB in Meta's Cloud. Your Phone Already Has That

    The push to run AI agents on edge devices will not be driven by model labs or hardware vendors, but by service providers like Meta whose ad-based business models create a powerful financial incentive to offload massive cloud compute costs onto the user's own powerful-but-idle hardware. Austin Lyons and Vik Sekar break down why the failed "AI PC" push has given way to a new, economically-driven strategy for on-device AI. They discuss how Qualcomm's acquisition of Modular provides the key software layer and why the ultimate vision is an "ecosystem of you" where the device acts as a context-aware orchestrator between local and cloud compute. This episode is presented by Crusoe. Try Crusoe's managed inference for open models with $5 in free credits: https://semidoped.com/crusoe "[Model Labs] business model is we make money when we do inference in the cloud. And yet we are saying, actually, there's a world where we would really like to just run this stuff locally... Meta's like, bro, I want to help you at the edge." — Austin Lyons, Chipstrat Key Takeaways: - The driver for edge AI is a business model conflict. Model labs like OpenAI sell cloud inference; service providers like Meta see it as a massive cost center for their ad-supported agents like Muse. - Your phone is an underutilized edge server. Its specs—like 8GB of RAM and 2 vCPUs of power—are comparable to the dedicated cloud VMs that services like Meta's Muse provision for each user. - The "AIPC" push failed because it was a hardware-first strategy without a use case. Today's edge AI push is a software-first strategy driven by the need to cut cloud OpEx for mass-market agents. - The role of the edge device is becoming a "context translation layer." It uses private data like location and biometrics to intelligently decide which AI tasks run locally versus in the cloud. - Qualcomm's acquisition of Modular is a key strategic move. It positions the open-source Mojo language as the "write once, run anywhere" software layer for deploying agents across diverse hardware. - Personal agents will lead to the "end of apps." Fixed UIs, which force a designer's paradigm on the user, will be replaced by interfaces generated on demand via natural language. - Google is missing its opportunity to lead in on-device AI. Despite vertical integration from TPUs to Pixel hardware and a leading ad business, it lacks the product execution of players like Meta. Chapters: 0:00 Who Makes Local AI Happen? 1:25 Making the Intro with AI 4:43 Qualcomm's Agent Vision 6:54 Why AIPCs Failed 8:45 The Phone as a Portal 13:52 Meta's Incentive for Edge AI 24:05 The End of Apps & UIs? 32:31 The Ideal Personal Agent 34:25 The Edge as a Context Layer 38:17 Qualcomm's Modular Acquisition 43:16 The Ecosystem of You 48:01 Google's Missed Opportunity Follow Chipstrat: Newsletter: https://www.chipstrat.com X: https://x.com/chipstrat Follow Vik: Newsletter: https://www.viksnewsletter.com/ X: https://x.com/vikramskr Follow Semi Doped: Get more of Austin and Vik daily, free: https://daily.semidoped.com/

  4. Sep 29

    Flash Hit Its Physical Limit. ReRAM Is the Replacement: WeeBit Nano's Ilan Sever

    As embedded flash hits a physical and economic scaling wall below 22nm, a new on-chip memory is needed. Ilan Sever of WeeBit Nano joins to explain how Resistive RAM (ReRAM) is positioned to become the new standard by offering a low-cost, high-endurance alternative. Ilan details how ReRAM's simple back-end-of-line integration, automotive qualification, and future applications in AI in-memory compute create a compelling replacement for the incumbent technology. "In geometries below 28 or 22 nanometer, flash reached its physical limit, actually. So physically you cannot implement embedded flash anymore and the industry had been looking for alternative technologies for a few decades now." — Ilan Sever, WeeBit Nano Key Takeaways: - The inflection point for on-chip memory is physical. Embedded flash cannot scale below the 28/22nm node because its floating gate becomes too small to reliably hold charge. - The economic problem with eFlash is its 25-30% cost increase, a result of adding 8-10 extra masks to a standard CMOS process—a non-starter for cost-sensitive MCUs. - ReRAM's manufacturing advantage is its simplicity. It's built in the back-end-of-line (BEOL) metal layers, adding only two masks and not interfering with the sensitive transistors below. - The physics are different: ReRAM isn't storing charge, it's changing resistance by using voltage to form or dissolve a conductive filament of oxygen vacancies in a dielectric. - Automotive qualification is the key market validation. WeeBit's ReRAM is qualified for the AEC-Q100 standard and demonstrates 100,000 write cycles—10x that of typical eFlash. - The business model isn't just a recipe; it's a full IP module with the bit cell, analog circuits, digital logic, and programming algorithms, which is what SoC designers actually need. - Beyond replacing flash, ReRAM (as a memristor) is a foundational technology for future in-memory compute (IMC) AI accelerators that perform calculations inside the memory to save power. Chapters: 0:00 The Overlooked Memory 4:15 The eFlash Scaling Wall 6:03 Four Uses of NVM 23:19 The Cost of eFlash 27:29 Introducing ReRAM 32:25 How ReRAM Works 37:12 Fab-Friendly Materials 42:14 Automotive Qualification 44:30 The Vertically Integrated IP Model 51:13 Future: In-Memory Compute 54:19 Innovation Story: Self-Trimming Follow Chipstrat: Newsletter: https://www.chipstrat.com X: https://x.com/chipstrat Follow Vik: Newsletter: https://www.viksnewsletter.com/ X: https://x.com/vikramskr Follow Semi Doped: Get more of Austin and Vik daily, free: https://daily.semidoped.com/

  5. Sep 24

    Qualcomm's Durga Malladi: HBC vs HBM, Dragonfly AI 250, and Winning on TCO

    Qualcomm re-entered the data center inference market late — competitive GPUs, XPUs, and custom accelerators are already out there. Durga Malladi (EVP/GM, Technology Planning, Edge & Data Center) explains why they didn't build another me-too accelerator and instead placed a differentiating technical bet: High Bandwidth Compute (HBC). The difference between HBC and HBM: HBM is stacked DRAM that shuttles data across wide buses to a separate accelerator — very high bandwidth, but expensive and power-hungry. HBC bonds stacked DRAM directly on top of a custom logic die using wafer-on-wafer techniques, so a lot of the compute runs literally next to memory. The result is lower latency and lower power per bit — and 18x effective bandwidth on the AI 250 versus the AI 200 at the SAME 768 GB LPDDR capacity and the SAME 160 kW per rack. Why the late-mover bet works: Qualcomm's differentiation isn't just architectural. Their foundry and memory-vendor relationships let them credibly promise the multi-megawatt-to-gigawatt supply hyperscalers need. Memory vendors are developing their own HBC-equivalent variants as partners rather than pure competitors, and Qualcomm expects the two technologies to co-evolve. Silicon is back in the lab; proof points land in Q1/Q2 2027. Chapters: 0:00 Introduction 2:49 The Memory Wall Problem 8:05 HBC Physical Architecture 9:33 HBC vs. Custom HBM 11:50 Silicon Proof Points Coming 12:08 Managing Thermals 13:34 Dragonfly AI 250 14:48 Trillion-Parameter Model on One Card 16:56 Tokenomics and TCO 18:26 Why TCO is Key 19:26 Manufacturing at Scale Follow Chipstrat: Newsletter: https://www.chipstrat.com X: https://x.com/chipstrat Follow Vik: Newsletter: https://www.viksnewsletter.com/ X: https://x.com/vikramskr Follow Semi Doped: Get more of Austin and Vik daily, free: https://daily.semidoped.com/

  6. Sep 18

    Pacing AI Means More Compute, Not Less

    The debate over "pacing" AI for safety is a red herring, as the practical outcome of more safety work is not a slowdown but a net increase in demand for compute hardware. Austin Lyons and Vik Sekar unpack Dario Amodei's call to slow down frontier AI development, arguing that the true governors on AI are not voluntary agreements but physical data center bottlenecks and geopolitical realities. They conclude that the push for more safety and interpretability will ultimately drive more sales of both training (GPUs) and inference (XPUs) hardware. Today's sponsor is G2i — expert training data, evals, and RL environments for AI labs: https://fandf.co/4wZvKHS Chapters: 0:00 Introduction: Pacing AI 1:36 Recapping Dario Amodei's Essay 7:42 Hot Take: Anthropic vs. OpenAI 11:34 The John Deere Playbook 15:22 The Problem with Interpretability 17:53 Pulling the Plug 19:03 The Real Bottleneck: Data Centers 22:03 The Geopolitical Blind Spot 23:41 Counter-narratives: Meta & Open Source 27:26 The Investment Thesis: Pacing Increases Compute 32:29 The Financial System Analogy 36:05 AI Safety as the New Cybersecurity Follow Chipstrat: Newsletter: https://www.chipstrat.com X: https://x.com/chipstrat Follow Vik: Newsletter: https://www.viksnewsletter.com/ X: https://x.com/vikramskr Follow Semi Doped: Get more of Austin and Vik daily, free: https://daily.semidoped.com/

  7. Sep 14

    TSMC Will Buy High NA After All: ASML's New 6x12-Inch Mask

    The emergence of user-friendly AI agents is creating massive new hardware demand, which in turn is forcing the semiconductor industry into a complex, multi-year transition to High NA EUV lithography and an entirely new 12-inch photomask standard. Austin Lyons and Vik Sekar unpack the physics and economics of ASML's move to High NA, explaining why it necessitates a shift from 6-inch to 6x12-inch masks and what this means for the timelines of TSMC, Intel, and Samsung. "This is where you have to get the whole supply chain coordinated around this. And this is why you really ultimately need ASML's biggest customers to stand up and say, 'We're going to buy this.'" — Austin Lyons, Chipstrat Key Takeaways: - ASML's High NA EUV (0.55 NA) solves resolution but creates a new problem: anamorphic optics (4x by 8x demagnification) cut the exposure field in half, effectively doubling the cost per wafer. - The industry's fix for High NA's halved output is a new 6x12-inch photomask standard — the 12-inch dimension compensates for the 8x demagnification, restoring the full 26mm x 33mm reticle size. - This shift to 12-inch masks is a full supply chain problem, which is why TSMC is waiting until 2030 for high-volume manufacturing while the ecosystem matures over the next 5-7 years. - Intel's aggressive first-mover strategy on High NA — with over 1 million wafers processed to date — is a direct reaction to its disastrous delay in adopting the previous generation of EUV. - Nearly 30% of ASML's revenue comes from recurring services (€2.8B of €9B in Q2), giving it a more stable financial profile than a typical equipment manufacturer. - The new wave of AI agents (Astra, Muse, Instinct) is moving from simple instruction-following to proactive partnership, creating hundreds of millions of new users for server CPUs and memory. - Integrating AI agents into ubiquitous text platforms like WhatsApp is key for mass adoption, as it solves the 'blank text box problem' for non-technical users who can simply text a request. Chapters: 0:00 Episode Opening 2:23 The Rise of AI Agents 6:00 Instinct: The Autonomous Agent 10:50 Productizing AI for Mass Adoption 14:03 From AI Agents to ASML 24:29 The Photomask Stitching Problem 31:49 High NA's Anamorphic Optics Tradeoff 36:39 The Cost of a Halved Reticle 37:26 The 6x12-Inch Mask Solution 39:08 A Full Supply Chain Problem 41:17 TSMC, Samsung & Intel Timelines 48:24 Intel's EUV History Lesson Follow Chipstrat: Newsletter: https://www.chipstrat.com X: https://x.com/chipstrat Follow Vik: Newsletter: https://www.viksnewsletter.com/ X: https://x.com/vikramskr Follow Semi Doped: Get more of Austin and Vik daily, free: https://daily.semidoped.com/

  8. Aug 27

    OpenAI’s Jalapeño! Feeling Hot Hot Hot!

    Austin and Vik react to OpenAI's Jalapeño announcement at Hot Chips. Plus extra spicy questions like should OpenAI sell it, how much of this was AI-written RTL, and where is Anthropic's chip? Key Takeaways: - The chip's core design philosophy is "dark silicon is cheaper than idle accelerators" — using one balanced chip and power-gating unused blocks is more efficient than a two-chip (e.g. GPU + LPU) solution. - The unprecedented nine-month RTL-to-tapeout cycle was enabled by AI for EDA tools, serving as a wake-up call that small, expert teams can now develop Rubin-class chips in under a year. - Jalapeño's key innovation is a NUMA-style architecture that gives each accelerator a local HBM slice, solving the memory contention that throttles performance in unified memory systems. - OpenAI chose Broadcom's ESUN for its scale-up network to connect 128 chips in the rack at 600 GB/s and up to 2,048 chips across 16 racks at 200G --- all scale up! - The design's "regret factor" principle justifies generality — the opportunity cost of being unable to support a future model is far higher than the marginal cost of adding hardware flexibility upfront. Chapters: 0:00 Hot Chips Reaction 2:32 Designing for User Experience 11:16 A Generalized Inference Chip 14:18 The Foundry-IDM Analogy 18:42 The 'Regret Factor' 21:02 The 9-Month Design Cycle 23:45 Challenging the Two-Chip Solution 35:08 Solving HBM Underutilization 36:46 The NUMA Architecture Solution 39:28 System-Level ESUN Networking 42:06 Dark Silicon vs. Idle Accelerators 49:08 A Wake-Up Call for the Industry 52:59 Where's Anthropic's Chip? Follow Chipstrat: Newsletter: https://www.chipstrat.com X: https://x.com/chipstrat Follow Vik: Newsletter: https://www.viksnewsletter.com/ X: https://x.com/vikramskr Follow Semi Doped: Get more of Austin and Vik daily, free: https://daily.semidoped.com/

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Ratings & Reviews

5
out of 5
22 Ratings

About

The business and technology of semiconductors. Alpha for engineers and investors alike.

Information

  • Creator
    Vikram Sekar and Austin Lyons
  • Years Active
    2026
  • Episodes
    52
  • Rating
    Clean
  • Copyright
    © 2026 Semi Doped
  • Show Website
    Semi Doped

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