805 citations · 1.3k across the 11 of their papers we have counts for
12 papers
Probability of super-regular matrices and MDS codes over finite fields
Rathinakumar Appuswamy, Marco Bazzani, Spencer Congero +3
Let be an linear code chosen uniformly at random over a finite field of size . The following asymptotic probability of being maximum distance sepa…
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…
Discovering Low-Precision Networks Close to Full-Precision Networks for Efficient Embedded Inference
Jeffrey L. McKinstry, Steven K. Esser, Rathinakumar Appuswamy +4
To realize the promise of ubiquitous embedded deep network inference, it is essential to seek limits of energy and area efficiency. To this end, low-precision networks offer tremen…