Brain computer interfaces (BCIs) have been widely adopted to enhance human perception via brain signals with abundant spatial-temporal dynamics, such as electroencephalogram (EEG). In recent years, BCI algorithms are moving from classical feature …
With the advance of Artificial Neural Networks, GEMM has become a dominant arithmetic operation in compute-intensive applications. Multipliers contribute to a major portion of area and power consumed by conventional systems. This work proposes T-MAC …
General matrix multiply (GEMM) is an important operation in broad applications, especially the thriving deep neural networks. To achieve low power consumption for GEMM, researchers have already leveraged unary computing, which manipulates bitstreams …
Stochastic computing is a statistical computing scheme that represents data as serial bit streams to greatly reduce hardware complexity. The key trade-off is that processing more bits in the streams yields higher computation accuracy at the cost of …
Stochastic computing (SC), with its probabilistic data representation format, has sparked renewed interest due to its ability to use very simple circuits to implement complex operations. Though unlike traditional binary computing, SC needs to …
General matrix multiplication (GEMM) is universal in various applications, such as signal processing, machine learning, and computer vision. Conventional GEMM hardware architectures based on binary computing exhibit low area and energy efficiency as …
Stochastic Computing (SC) is designed to minimize hardware area and power consumption compared to traditional binary-encoded computation, stemming from the bit-serial data representation and extremely straightforward logic. Though existing Stochastic …