805 citations · 815 across the 3 of their papers we have counts for
3 papers
A Scalable NorthPole System with End-to-End Vertical Integration for Low-Latency and Energy-Efficient LLM Inference
Michael V. DeBole, Rathinakumar Appuswamy, Neil McGlohon +30
A vertically integrated, end-to-end, research prototype system combines 288 NorthPole neural inference accelerator cards, offline training algorithms, a high-performance runtime st…
Structured Convolution Matrices for Energy-efficient Deep learning
Rathinakumar Appuswamy, Tapan Nayak, John Arthur +6
We derive a relationship between network representation in energy-efficient neuromorphic architectures and block Toplitz convolutional matrices. Inspired by this connection, we dev…
Convolutional Networks for Fast, Energy-Efficient Neuromorphic Computing
Steven K. Esser, Paul A. Merolla, John V. Arthur +13
Deep networks are now able to achieve human-level performance on a broad spectrum of recognition tasks. Independently, neuromorphic computing has now demonstrated unprecedented ene…