805 citations · 1.3k across the 11 of their papers we have counts for
3 papers · 1 filter
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…