Search Results for author: John V. Arthur

Found 5 papers, 0 papers with code

Discovering Low-Precision Networks Close to Full-Precision Networks for Efficient Embedded Inference

no code implementations ICLR 2019 Jeffrey L. McKinstry, Steven K. Esser, Rathinakumar Appuswamy, Deepika Bablani, John V. Arthur, Izzet B. Yildiz, Dharmendra S. Modha

Therefore, we (a) reduce solution distance by starting with pretrained fp32 precision baseline networks and fine-tuning, and (b) combat gradient noise introduced by quantization by training longer and reducing learning rates.

General Classification Quantization

Backpropagation for Energy-Efficient Neuromorphic Computing

no code implementations NeurIPS 2015 Steve K. Esser, Rathinakumar Appuswamy, Paul Merolla, John V. Arthur, Dharmendra S. Modha

Solving real world problems with embedded neural networks requires both training algorithms that achieve high performance and compatible hardware that runs in real time while remaining energy efficient.

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