Embedded vision applications, with their demand for ever more processing power, are driving up the size and complexity of edge SoCs. Heterogeneous architectures that bring together CPUs, GPUs and specialized NPUs have become the standard approach to achieving high-performance density and compelling headline specifications for edge vision; yet this approach is not without its problems, especially given the rapid evolution of AI models and the anticipated thermal challenges associated with more advanced process nodes. While software is set to be the true enabler of success, with community-wide initiatives such as the UXL Foundation empowering application developers to port code seamlessly to edge devices, it is clear that flexible, parallel and, most importantly, programmable hardware is central to delivering high-performance, high-efficiency vision applications at the edge. But the right solution isn’t here yet. What will it look like?
Programmability vs. Performance: Addressing Embedded Vision System Design’s Fundamental Challenge
Session Speakers
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Rob Fisher
Senior Director of Product Management, Imagination Technologies
With decades of experience in digital design, from high-performance radar systems to AI acceleration, Rob Fisher has a broad range of experience across many semiconductor industries. As the interface for Imagination’s customers in the automotive market, Rob is driving the definition of processor IP for next-generation edge AI devices.
