The massive data-movement overhead in traditional architectures has led to the adoption of In-Memory Computing (IMC) for energy-efficient Deep Neural Network (DNN) processing. By leveraging emerging devices like Spin-Orbit Torque Magnetic Tunnel Junctions (SOT-MTJs), IMC bypasses the"memory wall"and reduces leakage power inherent in traditional CMOS. However, this shift introduces dual hardware threats: manufacturing Process Variation (PV) degrades reliability and increases vulnerability to fault injection, while power Side-Channel Attacks (SCAs) compromise security. Existing defenses address these threats in isolation. This work presents a posttraining framework that simultaneously hardens analog IMC accelerators against both threats without retraining the model. Implemented in the IMAC-Sim simulator, our approach uses the proposed Variation Impact Score (VIS) to guide the mapping of Fault Observation Windows (FOWs) and introduces the Leakage Per Inference (LPI) metric to quantify input-dependent power variability under stochastic injection and the resulting reduction in effective signal-to-noise ratio. Experiments show that PV-induced faults can degrade accuracy by over 50%, while our method restores near-baseline accuracy and mitigates the threat of correlation-based power analysis attacks.
Muhtasim Alam Chowdhury, Ramtin Zand, Soheil Salehi· 0 citations
While neuromorphic systems offer a promising path for processing dynamic, event-based data, current benchmarks often fail to isolate the specific impact of temporal integration on model performance. To address this, our research rigorously investigates how neuromorphic architectures encode and integrate temporal information by conducting a comprehensive ablation study using a hybrid network. We systematically transition from a fully spatial Convolutional Neural Network (CNN) to a fully Spiking Neural Network (SNN) by progressively replacing ReLU activations with spiking neurons across nine distinct model configurations. To challenge these architectures, we introduce the Temporal-ASL Dataset, a neuromorphic benchmark specifically curated with signs that exhibit high spatial isomorphism but distinct temporal signatures. This approach allows us to decouple spatial features from motion dynamics and quantify the marginal contribution of spiking membrane dynamics in resolving ambiguities that remain invisible to frame-isolated models. Our analysis reveals a performance hierarchy that peaks at 72.5% with a hybrid SNN configuration, representing a 6.25% improvement over the spatial CNN baseline. Logit trajectory analysis confirms this boost stems from the spiking layers’ ability to disambiguate spatially similar signs. However, accuracy declines steadily in deeper hierarchies, falling to 23.75% for the fully spiking SNN. Ultimately, these findings demonstrate that shallow neuromorphic integration effectively maximizes the gains from temporal integration while mitigating the information loss inherent in binary spike quantization.
J. Seekings, Peyton S. Chandarana, Arshia Eslami et al.· International Conference on...· 0 citations
This work introduces FeatureFormer, a neural performance predictor that incorporates explicit node-wise encodings of FLOPs, parameter counts, and memory proxies within a gated graph attention architecture and presents NNEQ, a new large-scale energy consumption dataset that enables unified evaluation of latency and energy prediction.
Matthew Grenier, William Hammer, Andrew Heuer et al.· 0 citations
A novel and much-refined framework that extends SNN far beyond their current implementation and introduces a significant number of biologically-inspired structural and functional innovations is needed.
This work proposes heterogeneous neural networks that combine spiking neural networks (SNNs) and artificial neural networks (ANNs) at bandwidth-limited regions, such as chip boundaries, where spike-based communication reduces data transfer overhead.
Joshua Nardone, Rui-Jie Zhu, Ruhai Lin et al.· International Conference on...· 0 citations