Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data https://sscs.ieee.org/wp-content/themes/movedo/images/empty/thumbnail.jpg 150 150 https://secure.gravatar.com/avatar/8fcdccb598784519a6037b6f80b02dee03caa773fc8d223c13bfce179d70f915?s=96&d=mm&r=g
Abstract:
On-device learning at the edge enables low-latency, private personalization with improved long-term robustness and reduced maintenance costs. Yet, achieving scalable, low-power (LP) end-to-end on-chip learning, especially from real-world sequential data with a limited number of examples, is an open challenge. Indeed, accelerators supporting error backpropagation optimize for learning performance at …