IMC has the potential to address a critical and foundational challenge affecting computing
platforms today-that is, the high energy and delay costs of moving data and accessing data …

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 …

Hardware-friendly network quantization (eg, binary/uniform quantization) can efficiently
accelerate the inference and meanwhile reduce memory consumption of the deep neural …

We propose Additive Powers-of-Two~(APoT) quantization, an efficient non-uniform
quantization scheme for the bell-shaped and long-tailed distribution of weights and …

Embedding Artificial Intelligence onto low-power devices is a challenging task that has been
partly overcome with recent advances in machine learning and hardware design. Presently …

Pruning convolutional filters has demonstrated its effectiveness in compressing ConvNets.
Prior art in filter pruning requires users to specify a target model complexity (eg, model size …

Abstract Artificial Intelligence of Things (AIoT) which integrates artificial intelligence (AI) and
the Internet of Things (IoT), has attracted increasing attention recently. With the remarkable …

Deep neural networks (DNNs) have revolutionized the field of artificial intelligence and have
achieved unprecedented success in cognitive tasks such as image and speech recognition …

The high computation, memory, and power budgets of inferring convolutional neural
networks (CNNs) are major bottlenecks of model deployment to edge computing platforms …

Neural network training is computationally and memory intensive. Sparse training can
reduce the burden on emerging hardware platforms designed to accelerate sparse …