3 papers
cs.LG2019
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…
cs.LG2018
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…
cs.CV2018
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…