Neuromorphic Computing with Deeply Scaled Ferroelectric FinFET in Presence of Process Variation, Device Aging and Flicker Noise

03/05/2021
by   Sourav De, et al.
0

This paper reports a comprehensive study on the applicability of ultra-scaled ferroelectric FinFETs with 6 nm thick hafnium zirconium oxide layer for neuromorphic computing in the presence of process variation, flicker noise, and device aging. An intricate study has been conducted about the impact of such variations on the inference accuracy of pre-trained neural networks consisting of analog, quaternary (2-bit/cell) and binary synapse. A pre-trained neural network with 97.5 the baseline. Process variation, flicker noise, and device aging characterization have been performed and a statistical model has been developed to capture all these effects during neural network simulation. Extrapolated retention above 10 years have been achieved for binary read-out procedure. We have demonstrated that the impact of (1) retention degradation due to the oxide thickness scaling, (2) process variation, and (3) flicker noise can be abated in ferroelectric FinFET based binary neural networks, which exhibits superior performance over quaternary and analog neural network, amidst all variations. The performance of a neural network is the result of coalesced performance of device, architecture and algorithm. This research corroborates the applicability of deeply scaled ferroelectric FinFETs for non-von Neumann computing with proper combination of architecture and algorithm.

READ FULL TEXT
research
03/03/2021

Alleviation of Temperature Variation Induced Accuracy Degradation in Ferroelectric FinFET Based Neural Network

This paper reports the impacts of temperature variation on the inference...
research
10/19/2021

PR-CIM: a Variation-Aware Binary-Neural-Network Framework for Process-Resilient Computation-in-memory

Binary neural networks (BNNs) that use 1-bit weights and activations hav...
research
06/09/2021

Network insensitivity to parameter noise via adversarial regularization

Neuromorphic neural network processors, in the form of compute-in-memory...
research
11/14/2021

TMS-Crossbars with Tactile Sensing

The first stage of tactile sensing is data acquisition using tactile sen...
research
11/26/2018

Noisy Computations during Inference: Harmful or Helpful?

We study two aspects of noisy computations during inference. The first a...
research
06/05/2023

Modeling Tor Network Growth by Extrapolating Consensus Data

Since the Tor network is evolving into an infrastructure for anonymous c...
research
02/03/2020

Modular Simulation Framework for Process Variation Analysis of MRAM-based Deep Belief Networks

Magnetic Random-Access Memory (MRAM) based p-bit neuromorphic computing ...

Please sign up or login with your details

Forgot password? Click here to reset