Implications of Noise in Resistive Memory on Deep Neural Networks for Image Classification
Resistive memory is a promising alternative to SRAM, but is also an inherently unstable device that requires substantial effort to ensure correct read and write operations. To avoid the associated costs in terms of area, time and energy, the present work is concerned with exploring how much noise in memory operations can be tolerated by image classification tasks based on neural networks. We introduce a special noisy operator that mimics the noise in an exemplary resistive memory unit, explore the resilience of convolutional neural networks on the CIFAR-10 classification task, and discuss a couple of countermeasures to improve this resilience.
Code (0)
등록된 구현이 없습니다.
Tasks
image-classificationImage ClassificationSimilar Papers 제목 키워드 기반
Pruning random resistive memory for optimizing analogue AI
The rapid advancement of artificial intelligence (AI) has been marked by the large language models exhibiting human-like intelligence. However, these models also present unprecedented challenges to energy consumption and…
Audio ClassificationImage SegmentationSemantic SegmentationResistive Memory-based Neural Differential Equation Solver for Score-based Diffusion Model
Human brains image complicated scenes when reading a novel. Replicating this imagination is one of the ultimate goals of AI-Generated Content (AIGC). However, current AIGC methods, such as score-based diffusion, are stil…
Edge-computingTraining a Probabilistic Graphical Model with Resistive Switching Electronic Synapses
Current large scale implementations of deep learning and data mining require thousands of processors, massive amounts of off-chip memory, and consume gigajoules of energy. Emerging memory technologies such as nanoscale t…
In-memory Training on Analog Devices with Limited Conductance States via Multi-tile Residual Learning
Analog in-memory computing (AIMC) accelerators enable efficient deep neural network computation directly within memory using resistive crossbar arrays, where model parameters are represented by the conductance states of …
Image ClassificationQMC: Efficient SLM Edge Inference via Outlier-Aware Quantization and Emergent Memories Co-Design
Deploying Small Language Models (SLMs) on edge platforms is critical for real-time, privacy-sensitive generative AI, yet constrained by memory, latency, and energy budgets. Quantization reduces model size and cost but su…