2020 BRN Discussion, page-6520

tinyLM Talks - Kristofor Carlson

Announcing two tinyML Talks on September 1st, 2020


8:30 AM - 9:00 AM Pacific Daylight Time (PDT)
Kristofor Carlson, Senior Research Scientist, BrainChip Inc.
"The Akida Neural Processor: Low Power CNN Inference and Learning at the Edge"

The Akida event-based neural processor is a high-performance, low-power SoC targeting edge applications. In this session, we discuss the key distinguishing factors of Akida’s computing architecture which include aggressive 1 to 4-bit weight and activation quantization, event-based implementation of machine-learning operations, and the distribution of computation across many small neural processing units (NPUs). We show how these architectural changes result in a 50% reduction of MACs, parameter memory usage, and peak bandwidth requirements when compared with non-event-based 8-bit machine learning accelerators. Finally, we describe how Akida performs on-chip learning with a proprietary bio-inspired learning algorithm. We present state-of-the-art few-shot learning in both visual (MobileNet on mini-imagenet) and auditory (6-layer CNN on Google Speech Commands) domains.

Kristofor Carlson is a senior research scientist at BrainChip Inc. Previously, he worked as postdoctoral scholar in Jeff Krichmar's cognitive robotics laboratory at UC Irvine where he studied unsupervised learning rules in spiking neural networks (SNNs), the application of evolutionary algorithms to SNNs, and neuromorphic computing. Afterwards, he worked as postdoctoral appointee at Sandia National Laboratories where he applied uncertainty quantification to computational neural models and helped develop neuromorphic systems. In his current role, he is involved in the design and optimization of both machine learning algorithms and hardware architecture of BrainChip’s latest system on a chip, Akida.

We encourage you to register earlier since on-line broadcast capacity may be limited.