Episode 15: Growth Drivers for Semiconductors in the Datacenter

The Circuit · 17 Apr 2023 · 43m · Episode 15

What this episode covers

Ben Bajarin and Jay Goldberg discuss the shifting economics of data center semiconductors, highlighting that while total silicon content is growing, CPU share is declining as GPU, FPGA, and accelerator workloads expand. They analyze how hyperscalers like Google, Amazon, and Microsoft are pursuing custom silicon to control roadmaps and solve specific pain points, such as Google's TPU for YouTube, while noting the high risks for startups entering the accelerator market. The conversation emphasizes that heterogeneous compute is driving value, with high-intensity workloads like LLM training justifying expensive chips like Nvidia's H100, whereas commodity workloads remain on cheaper, parallelizable infrastructure.

Mentioned

Companies
Nvidia, Intel, AMD, Google, Amazon, Microsoft, Lattice, Xilinx, Altera, Baidu, Alibaba, Tencent, Apple, Qualcomm, Samsung, Havana
Products
H100, A100, TPU, Graviton, Zion, Cranium, Inferentia, GPT, AutoGPT, Kim GPT
Also
FPGA

Other shows on the same ground, closest first.

  • EE Times Current · 29m

    How Much is Enough for Hyperscale Companies?

    Brian Santo hosts a discussion on how hyperscale companies like Google, Facebook, Amazon, and Microsoft are reshaping the semiconductor industry by demanding extreme power efficiency and designing custom AI accelerators. Experts Rick Merritt, Gary Hilson, and Dylan McGrath explain that these giants are pushing beyond standard roadmaps, collaborating directly with equipment vendors like Applied Materials, and leveraging their vast capital to drive innovation in memory and process technology. The episode concludes with Wally Rhines' insight that these companies' ultimate goal is owning the data and systems rather than just selling silicon, raising concerns about their growing societal power.

  • Moore's Lobby · 58m

    Ep. 27 | Two Google Senior VPs of Engineering: From Shipping Containers to Today's Data Centers

    Google Fellows Luiz Barroso and Amin Vahdat discuss the evolution of Google's data center infrastructure, from early 'broom closet' colocation facilities and shipping container prototypes to modern, energy-efficient warehouse-scale systems. They explain how Google shifted from relying on commodity hardware to designing custom domain-specific accelerators like TPUs for machine learning and video processing to overcome the limits of Moore's Law. The conversation also covers the critical importance of energy proportionality, Power Usage Effectiveness (PUE), and the cultural philosophy of 'healthy hubris' that drives Google's engineering decisions.

  • EE Times Current · 34m

    Whither Semiconductors? The Wheels on the Bus Go ‘Round and ‘Round

    Jim McGregor, co-founder of Tirias Research, discusses the evolving competitive landscape of the semiconductor industry, identifying Intel, AMD, and NVIDIA (following its potential acquisition of Arm) as the new dominant trio. He explains how the convergence of computing, connectivity, and intelligence is blurring traditional boundaries, with companies like Qualcomm, Samsung, and MediaTek competing across smartphones, servers, and automotive sectors. McGregor highlights the shift toward heterogeneous computing, specialized accelerators like DPUs, and the move away from monolithic dies toward multi-chip modules and advanced packaging to overcome Moore's Law limitations.

  • Oxide and Friends · 1h 54m

    Heterogeneous Computing with Raja Koduri

    Raja Koduri, former chief architect at Intel and AMD, joins Bryan Cantrill and Adam Leventhal to explain the differences between CPUs, GPUs, FPGAs, and ASICs through historical and analogical lenses. Koduri traces the evolution from 3dfx and Direct3D to CUDA, arguing that NVIDIA’s success stems from a tight coupling of programming and execution models, which creates a sticky ecosystem. He uses transportation analogies to categorize these technologies—cars for CPUs, buses for GPUs, and construction vehicles for FPGAs—and discusses how advanced packaging and memory bandwidth are becoming the primary constraints in modern AI hardware.

  • Built 2 Scale · 1h 08m

    Google TPU vs Nvidia GPUs: Token Per Watt Dominance, or the Era of Scaling Is Over?

    Founders Matt Perrott and Scott Wilcox discuss Google's Gemini 3.0 training on TPUs, which bypasses Nvidia's hardware to achieve a structural cost advantage, and the resulting threat to 'vibe coding' startups like Lovable. They analyze the 'age of scaling is over' consensus among researchers like Ilya Sutskever, arguing that future breakthroughs require real-world spatial intelligence rather than just more compute. The hosts also debate office culture, remote work dynamics, and the three critical roles founders must hire early: product, brand, and sales.

The summary, chapters and topics are generated from the episode’s audio by this directory, not written by The Circuit. The episode itself belongs to its publisher.