4 citations · 4 across the 2 of their papers we have counts for
4 papers
Structured Convolution Matrices for Energy-efficient Deep learning
Rathinakumar Appuswamy, Tapan Nayak, John Arthur +6
We derive a relationship between network representation in energy-efficient neuromorphic architectures and block Toplitz convolutional matrices. Inspired by this connection, we dev…
Deep neural networks are robust to weight binarization and other non-linear distortions
Paul Merolla, Rathinakumar Appuswamy, John Arthur +2
Recent results show that deep neural networks achieve excellent performance even when, during training, weights are quantized and projected to a binary representation. Here, we sho…
Gibbs Sampling with Low-Power Spiking Digital Neurons
Srinjoy Das, Bruno Umbria Pedroni, Paul Merolla +6
Restricted Boltzmann Machines and Deep Belief Networks have been successfully used in a wide variety of applications including image classification and speech recognition. Inferenc…
Optimal Lempel-Ziv based lossy compression for memoryless data: how to make the right mistakes
Narayana Santhanam, Dharmendra Modha
Compression refers to encoding data using bits, so that the representation uses as few bits as possible. Compression could be lossless: i.e. encoded data can be recovered exactly f…