As machine learning and AI continue to rapidly develop, and with the ever-closer end of
Moore's law, new avenues and novel ideas in architecture design are being created and …

Spiking neural networks (SNNs) have gained huge attention as a potential energy-efficient
alternative to conventional artificial neural networks (ANNs) due to their inherent high …

The depthwise separable convolution is commonly seen in convolutional neural networks
(CNNs), and is widely used to reduce the computation overhead of a standard multi-channel …

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 …

Bicubic interpolation is a classic algorithm in the real-time image processing systems, which
can achieve good quality at a relatively low hardware cost, and is also the fundamental …

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

Brain computer interfaces (BCIs) have been widely adopted to enhance human perception
via brain signals with abundant spatial-temporal dynamics, such as electroencephalogram …

Stochastic Computing (SC) has the potential to dramatically improve important nanoscale
circuit metrics, including area and power dissipation, for implementing complex digital …

The appealing property of low area, low power, and high bit error tolerance has made
Stochastic Computing (SC) a promising alternative to conventional binary arithmetic for …