805 citations · 1.3k across the 13 of their papers we have counts for
13 papers
Mitigating hallucinations and omissions in LLMs for invertible problems: An application to hardware logic design automation
Andrew S. Cassidy, Guillaume Garreau, Jay Sivagnaname +4
We show for invertible problems that transform data from a source domain (for example, Logic Condition Tables (LCTs)) to a destination domain (for example, Hardware Description Lan…
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…
SiLQ: Simple Large Language Model Quantization-Aware Training
Steven K. Esser, Jeffrey L. McKinstry, Deepika Bablani +2
Large language models can be quantized to reduce inference time latency, model size, and energy consumption, thereby delivering a better user experience at lower cost. A challenge…
Efficient and Effective Methods for Mixed Precision Neural Network Quantization for Faster, Energy-efficient Inference
Deepika Bablani, Jeffrey L. Mckinstry, Steven K. Esser +2
For efficient neural network inference, it is desirable to achieve state-of-the-art accuracy with the simplest networks requiring the least computation, memory, and power. Quantizi…
Learned Step Size Quantization
Steven K. Esser, Jeffrey L. McKinstry, Deepika Bablani +2
Deep networks run with low precision operations at inference time offer power and space advantages over high precision alternatives, but need to overcome the challenge of maintaini…
Low Precision Policy Distillation with Application to Low-Power, Real-time Sensation-Cognition-Action Loop with Neuromorphic Computing
Jeffrey L Mckinstry, Davis R. Barch, Deepika Bablani +5
Low precision networks in the reinforcement learning (RL) setting are relatively unexplored because of the limitations of binary activations for function approximation. Here, in th…