An energy, area, and speed efficient time-domain VMM circuit and neurotrophic processor architecture.
Contemporary applications of computationally intensive artificial intelligence (AI) algorithms such as Deep/Reccurrent Neural Networks call for an efficient neuromorphic processor, especially for mobile/IoT devices. Within these deep neural networks and many other computationally-intensive data and signal processing systems, the vector-by-matrix multiplication (VMM) is the most common operation. Current digital approaches to the VMM task results in a relatively sparse design, which greatly diminishes the performance for memory access.
Researchers at the University of California, Santa Barbara have created an energy, area, and speed efficient time-domain VMM circuit and neuromorphic processor architecture based on 3D-NAND flash memory devices targeting various AI applications. Efficient accelerators for emerging Non-Volatile Memory (NVM) devices such as compact flash memory devices are developed through the use of analog, rather than digital, computing.
electronics, indmicroelec, nueromorphic engineering, neurotrophic processor, artificial intelligence, neural networks, vector, internet of things