Convolutional neural networks (CNNs) have proven to be a disruptive technology in most
vision, speech and image processing tasks. Given their ubiquitous acceptance, the research …

This book provides a structured treatment of the key principles and techniques for enabling
efficient processing of deep neural networks (DNNs). DNNs are currently widely used for …

Convolutional neural networks (CNNs) are emerging as powerful tools for image
processing. Recent machine learning work has reduced CNNs' compute and data volumes …

The data partitioning and scheduling strategies used by DNN accelerators to leverage reuse
and perform staging are known as dataflow, which directly impacts the performance and …

Machine learning (ML) models are widely used in many important domains. For efficiently
processing these computational-and memory-intensive applications, tensors of these …

In this paper, we introduce the Tensor Streaming Processor (TSP) architecture, a functionally-
sliced microarchitecture with memory units interleaved with vector and matrix deep learning …

Point cloud analytics is poised to become a key workload on battery-powered embedded
and mobile platforms in a wide range of emerging application domains, such as …

Deep neural network (DNN) accelerators as an example of domain-specific architecture
have demonstrated great success in DNN inference. However, the architecture acceleration …

Convolutional neural networks (CNNs) have recently demonstrated superior quality for
computational imaging applications. Therefore, they have great potential to revolutionize the …

We show that selecting a data width for all values in Deep Neural Networks, quantized or not
and even if that width is different per layer, amounts to worst-case design. Much shorter data …