This work is an introduction and a survey for the Special Issue on Machine Intelligence at the
Edge. The authors argue that workloads that were formerly performed in the cloud are …

Convolutional Neural Networks (CNNs) can achieve high classification accuracy while they
require complex computation. Binarized Neural Networks (BNNs) with binarized weights …

FPGAs have been rapidly adopted for acceleration of Deep Neural Networks (DNNs) with
improved latency and energy efficiency compared to CPU and GPU-based implementations …

In this paper, we pave a novel way towards the concept of bit-wise In-Memory Convolution
Engine (IMCE) that could implement the dominant convolution computation of Deep …

This paper proposes a new fine-grained dynamic pruning technique for CNN inference,
named channel gating, and presents an accelerator architecture that can effectively exploit …

Always-on speech interfaces are prevailing in human-machine interaction, especially on
wearable devices, Internet of Things, etc., which benefits from the recent breakthroughs in …

General matrix multiplication (GEMM) is universal in various applications, such as signal
processing, machine learning, and computer vision. Conventional GEMM hardware …

Due to less memory requirement, low computation overhead and negligible accuracy
degradation, deep neural networks with binary/ternary weights (BTNNs) have been widely …

With the advances in massive computing ability and big data science, deep neural network
(DNN) has been developing rapidly for different applications. However, due to its extensive …

Controller Area Network (CAN) is the de facto standard for in-vehicle networks. However, it
is inherently vulnerable to various attacks due to the lack of security features. Intrusion …