Reliability-Aware Deployment of DNNs on In-Memory Analog Computing Architectures

no code implementations • 2 Oct 2022 • Md Hasibul Amin, Mohammed Elbtity, Ramtin Zand

Conventional in-memory computing (IMC) architectures consist of analog memristive crossbars to accelerate matrix-vector multiplication (MVM), and digital functional units to realize nonlinear vector (NLV) operations in deep neural networks (DNNs).

MRAM-based Analog Sigmoid Function for In-memory Computing

no code implementations • 21 Apr 2022 • Md Hasibul Amin, Mohammed Elbtity, Mohammadreza Mohammadi, Ramtin Zand

We propose an analog implementation of the transcendental activation function leveraging two spin-orbit torque magnetoresistive random-access memory (SOT-MRAM) devices and a CMOS inverter.

Interconnect Parasitics and Partitioning in Fully-Analog In-Memory Computing Architectures

no code implementations • 29 Jan 2022 • Md Hasibul Amin, Mohammed Elbtity, Ramtin Zand

Fully-analog in-memory computing (IMC) architectures that implement both matrix-vector multiplication and non-linear vector operations within the same memory array have shown promising performance benefits over conventional IMC systems due to the removal of energy-hungry signal conversion units.

An In-Memory Analog Computing Co-Processor for Energy-Efficient CNN Inference on Mobile Devices

no code implementations • 24 May 2021 • Mohammed Elbtity, Abhishek Singh, Brendan Reidy, Xiaochen Guo, Ramtin Zand

In this paper, we develop an in-memory analog computing (IMAC) architecture realizing both synaptic behavior and activation functions within non-volatile memory arrays.