Linley Spring Processor Conference 2021

April 19 - 23, 2021
Proceedings Available

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Agenda for Day One: Monday April 19, 2021
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Driving AI From the Cloud to the Edge
Linley Gwennap, Principal Analyst, TechInsights

AI accelerators continue to evolve rapidly. In the data center, new chips focus on sparsity and other techniques for improving performance per watt. Scalability is paramount, particularly for training. Architecture innovation is particularly rampant at the edge, where power limitations are stringent and software stacks are smaller. Numerous vendors, large and small, are deploying a variety of approaches including CGRA, neuromorphic, and analog computation. This presentation will describe the latest architecture trends for AI accelerators of all types. 

There will be Q&A following this presentation.

9:30am-9:40amBreak Sponsored by The Linley Group
9:40am-11:40amSession 1: Edge AI (Part I)

As AI applications move from cloud platforms into edge devices, processor designers are increasingly including hardware accelerators for this important function. These processors target lower performance than cloud accelerators but must meet the strict cost and power requirements of consumer, industrial, IoT, mobile, and many other types of devices. This session, moderated by The Linley Group senior analyst Mike Demler, examines a range of chips and IP cores that accelerate edge-AI inference.

Commercializing AI via Low-Precision Training at High Sparsity Ratios
Moshe Mishali, CEO and co-founder, Deep AI

Training AI models is typically performed on high-end GPUs using expensive floating-point operations (FLOPs). Inference, on the other hand, typically requires no more than modest INT8 hardware. AI development is then limited to ongoing communications between the data-facing inference devices and a massive training center, with the resulting performance, cost, security and data-privacy implications. We discuss the challenges in low precision, sparsity constrained training and Deep-AI's breakthrough technology that enables a seamless integrated INT8 training-inference workflow and edge device personalization.

Building a Neural Engine with Unrivaled IPS/W
Sharad Chole, Chief Scientist, Cofounder, Expedera

Obtaining the highest IPS/W on real neural network workloads requires a complete rethink of the AI architecture. This presentation explores a unique approach leveraging networking principles that achieves high utilization and lowest latency while minimizing power, external bandwidth, and die area. The architecture uses a hardware scheduler, innovative memory management to handle compute and data efficiently, and a unified pipeline with zero context switch penalty. Executing neural networks natively with only metadata from each layer greatly simplifies the software stack.

Scalable and Power-Efficient Architecture for Deep Learning at the Edge
Daniel Chibotero, Chief Architect, Hailo Technologies

With the proliferation of edge AI applications, questions around the right balance between performance, power and accuracy arise. This presentation will describe Hailo’s architecture, which is leveraged by customers across industries to perform high-performance AI inferencing at the edge. We will show real-life benchmarks in an embedded power envelope and discuss their implications and importance. We will then continue to actual usage examples that illustrate the intrinsic properties required in edge AI processing.

Ultra-Low-Power Pattern Recognition with Spiking Neural Networks
Sumeet Kumar, CEO, Innatera

Noise, non-idealities, and the complexity of spatiotemporal processing make it challenging to achieve latency and power targets in always-on applications at the sensor-edge. Spiking neural networks are a compact, powerful means of processing spatiotemporal data within stringent operating envelopes, leveraging an inherent notion of time to realize advanced signal processing functions. This presentation introduces Innatera’s ultra-low-power mixed-signal approach to neuromorphic computing with spiking neural networks, and delves into the challenges of accelerating them at the sensor-edge.

For this session, each talk will have 10 minutes of Q&A immediately following.

11:40am-12:40pmBreakout sessions with today's speakers


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