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Tech Threads: Weaving the Intelligent Future

Tech Threads: Weaving the Intelligent Future

By: Baya Systems
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This podcast hosted by Baya Systems explores the cutting edge of technology, from AI acceleration to data movement and chiplet innovation. Each episode dives into groundbreaking advancements shaping the future of computing, featuring insights from industry experts on the trends and challenges defining the tech landscape. Tune in to stay ahead in the rapidly evolving world of technology.©2025 Baya Systems Politics & Government
Episodes
  • Breaking the Memory Wall: How New Memory Architectures are Reshaping AI Inference

    Breaking the Memory Wall: How New Memory Architectures are Reshaping AI Inference
    Jun 16 2026
    In this episode of Tech Threads: Weaving the Intelligent Future, Baya Systems’ Nandan Nayampally sits down with Charlie Cheng, founder and CEO of TC Lab, for an in-depth conversation on the memory wall and why it has become one of the defining bottlenecks in AI infrastructure. While memory constraints have existed for decades, AI inference is bringing the issue into sharper focus by turning memory bandwidth into a direct driver of user experience, system performance, and data center economics.

    Charlie shares his perspective on the industry’s shift toward alternative AI architectures, from high-bandwidth memory and SRAM-based approaches to emerging 3D memory technologies and hybrid-bonded architectures that bring memory much closer to compute. He explains why inference workloads, especially token generation and KV cache access, can quickly become bandwidth-bound, and why solving that challenge requires rethinking the relationship between compute, memory, packaging, and on-chip data movement.

    The discussion also explores what happens when memory bottlenecks are reduced or removed. As more bandwidth becomes available to AI accelerators, the pressure shifts to the rest of the system, including networks-on-chip, chiplet fabrics, and data movement architectures. For companies building next-generation AI chips, hyperscale infrastructure, autonomous systems, and edge inference platforms, this creates both a challenge and an opportunity: the need for more flexible, scalable, and software-defined approaches to moving data efficiently across increasingly complex systems.

    Tune in for an expert look at why the future of AI performance depends as much on memory innovation and data movement as it does on compute, and how new architectures could help unlock faster, more efficient, and more scalable AI systems.
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    39 mins
  • Beyond the CPU vs GPU War: Rethinking AI Compute at the System Level

    Beyond the CPU vs GPU War: Rethinking AI Compute at the System Level
    Apr 28 2026
    In this episode of Tech Threads, Nandan Nayampally, Baya Systems CCO, sits down with Ian Ferguson, Vice President of Vertical Markets and Business Development at SiFive, to unpack one of the most important shifts happening in modern computing: AI is no longer just about scaling compute, it’s about orchestrating complexity.

    As architectures fragment across accelerators, chiplets, and custom silicon, the real challenge is no longer building faster chips. it’s turning all of these elements into a cohesive, high-performance system.

    This conversation explores why the industry is moving beyond the traditional “CPU vs GPU” narrative and toward a system-level approach where performance is defined by how effectively compute, memory, interconnect and software work together.

    From the growing momentum behind RISC-V to the rise of heterogeneous compute environments, the discussion highlights a clear trend: the future won’t be defined by a single dominant architecture, but by optimized combinations of technologies tailored to specific workloads.

    That shift introduces a new layer of complexity.

    Key themes explored in this episode include:
    - Why data movement is emerging as the primary constraint in AI systems
    - How efficiency metrics like “tokens per dollar” are reshaping design priorities
    - The shift toward purpose-built architectures across data center, automotive, and edge applications
    - The role of open ecosystems and interoperability in accelerating innovation
    - Why competitive advantage is shifting from individual components to full system design

    If you’re interested in where AI is headed, this is a must-watch conversation on the forces shaping the future of compute and what it takes to stay ahead.
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    49 mins
  • Inside the AI Bottleneck: Data Movement, Chiplets, and System Scaling

    Inside the AI Bottleneck: Data Movement, Chiplets, and System Scaling
    Mar 27 2026
    For the last decade AI has been driven by one thing, more compute: bigger models, more accelerators, higher throughput.

    But as NVIDIA’s Jensen Huang recently highlighted at GTC, the industry is hitting a different kind of wall, one that hasn’t received nearly as much attention.

    The real constraint is no longer just compute. It’s data movement.

    To its credit, Nvidia has pushed this frontier with innovations like NVLink Fusion, and continued investment in connectivity AI dataflow architectures. But the challenge is bigger than any one company.

    As AI systems scale to hundreds - and even thousands - of processors, performance is increasingly defined by the ability to efficiently move, synchronize, and manage data across increasingly distributed architectures that can orchestrate data across chiplets, nodes, and entire racks.

    In this episode of Tech Threads, we bring together a panel of deeply experienced technologists, architects and leaders from companies like Intel, Arm, Altera, Texas Instruments, and Arteris - individuals who have helped shape modern compute, interconnect standards, and system architecture.

    Together they explore what is really changing beneath the surface: why traditional scaling approaches are breaking down, how coherent interconnects and network-on-chip architectures are evolving, and why system-level thinking is becoming essential.

    They also dive into the growing complexity introduced by chiplet-based designs, heterogeneous compute, and distributed memory systems and what it takes to maintain performance, efficiency, and programmability at scale.

    This is not just a technology shift, it’s an architectural reset.

    If you’re building or thinking about next-generation AI systems, this conversation gets to the heart of what matters next.
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    54 mins
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