In recent years, the increasing variety of nonlinear activation functions (NAFs) in deep neural
networks (DNNs) has led to higher computational demands. However, hardware …

Since the lightweight convolutional neural network EfficientNet was proposed by Google in
2019, the series of models have quickly become very popular due to their superior …

In the development of the neural network (NN), the activation function has become more and
more important. The selection of the activation function indirectly affects the convergence …

Recently, learning-based lossless compression methods for volumetric medical images
have attracted much attention. They can achieve higher compression ratios than traditional …

Analog In-memory Computing (IMC) has demonstrated energy-efficient and low latency
implementation of convolution and fully-connected layers in deep neural networks (DNN) by …

Deep neural networks (DNNs) with various nonlinear activation functions (NAFs) have
achieved unprecedented successes, sparking interest in efficient DNN hardware …

This paper thoroughly surveys machine learning (ML) algorithms acceleration in hardware
accelerators, focusing on Field-Programmable Gate Arrays (FPGAs). It reviews 287 out of …

Computing-in-memory (CIM) macros aiming at accelerating deep learning operations at low
power need activation function (AF) units on the same die to reduce their host-dependency …

The activation function is one of key elements in modern machine learning algorithms.
However, some broadly-used activation functions are exceptionally complex, eg, GELU in …

The proposed piece-wise-linear (PWL) method utilizes the Method of Least Squares to
implement transcendental functions such as Sigmoid and Hyperbolic-Tangent with control …