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Alleviation of Temperature Variation Induced Accuracy Degradation in Ferroelectric FinFET Based Neural Network
This paper reports the impacts of temperature variation on the inference...
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Noisy Computations during Inference: Harmful or Helpful?
We study two aspects of noisy computations during inference. The first a...
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Design space exploration of Ferroelectric FET based Processing-in-Memory DNN Accelerator
In this letter, we quantify the impact of device limitations on the clas...
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Modular Simulation Framework for Process Variation Analysis of MRAM-based Deep Belief Networks
Magnetic Random-Access Memory (MRAM) based p-bit neuromorphic computing ...
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Dynamic Precision Analog Computing for Neural Networks
Analog electronic and optical computing exhibit tremendous advantages ov...
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A flexible and fast PyTorch toolkit for simulating training and inference on analog crossbar arrays
We introduce the IBM Analog Hardware Acceleration Kit, a new and first o...
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Device-aware inference operations in SONOS nonvolatile memory arrays
Non-volatile memory arrays can deploy pre-trained neural network models ...
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Neuromorphic Computing with Deeply Scaled Ferroelectric FinFET in Presence of Process Variation, Device Aging and Flicker Noise
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.
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