3 citations · 3 across the 1 of their papers we have counts for
5 papers
3D-aCortex: An Ultra-Compact Energy-Efficient Neurocomputing Platform Based on Commercial 3D-NAND Flash Memories
Mohammad Bavandpour, Shubham Sahay, Mohammad Reza Mahmoodi +1
The first contribution of this paper is the development of extremely dense, energy-efficient mixed-signal vector-by-matrix-multiplication (VMM) circuits based on the existing 3D-NA…
Energy-Efficient Moderate Precision Time-Domain Mixed-signal Vector-by-Matrix Multiplier Exploiting 1T-1R Arrays
Shubham Sahay, Mohammad Bavandpour, Mohammad Reza Mahmoodi +1
The emerging mobile devices in this era of internet-of-things (IoT) require a dedicated processor to enable computationally intensive applications such as neuromorphic computing an…
Improving Noise Tolerance of Mixed-Signal Neural Networks
Michael Klachko, Mohammad Reza Mahmoodi, Dmitri B. Strukov
Mixed-signal hardware accelerators for deep learning achieve orders of magnitude better power efficiency than their digital counterparts. In the ultra-low power consumption regime,…
Compact Modeling of I-V Characteristics, Temperature Dependency, Variations, and Noise of Integrated, Reproducible Metal-Oxide Memristors
H. Nili, A. Vincent, M. Prezioso +3
We present a comprehensive phenomenological model for the crossbar integrated metal-oxide continuous-state memristors. The model consists of static and dynamic equations, which are…
Energy-Efficient Time-Domain Vector-by-Matrix Multiplier for Neurocomputing and Beyond
Mohammad Bavandpour, Mohammad Reza Mahmoodi, Dmitri B. Strukov
We propose an extremely energy-efficient mixed-signal approach for performing vector-by-matrix multiplication in a time domain. In such implementation, multi-bit values of the inpu…