Many practical data-processing algorithms fail to execute efficiently on general-purpose
CPUs (Central Processing Units) due to the sequential matter of their operations and …

Hyperdimensional computing (HDC) is an emerging computing paradigm that imitates the
brain's structure to offer a powerful and efficient processing and learning model. In HDC, the …

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

The summation of multiple operands in parallel forms part of the critical path in various
digital signal processing units. To speedup the summation, high compression ratio counters …

Superconducting logic offers the potential to perform computation at tremendous speeds
and energy savings. However, a" semantic gap" lies between the level-driven logic that …

Employing convolutional neural networks (CNNs) in embedded devices seeks novel low-
cost and energy efficient CNN accelerators. Stochastic computing (SC) is a promising low …

We propose a novel method for approximate hardware implementation of univariate math
functions with significantly fewer hardware resources compared to previous approaches …

Sorting data is needed in many application domains. Traditionally, the data is read from
memory and sent to a general-purpose processor or application-specific hardware for …

Unary computing, whose arithmetics require only one logic gate, has enabled efficient DNN
processing, especially on strictly power-constrained devices. However, unary computing still …

General matrix multiply (GEMM) is an important operation in broad applications, especially
the thriving deep neural networks. To achieve low power consumption for GEMM …