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Approximate computing for spiking neural networks., , and . DATE, page 193-198. IEEE, (2017)Sparsity Turns Adversarial: Energy and Latency Attacks on Deep Neural Networks., , and . IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 39 (11): 4129-4141 (2020)Value Similarity Extensions for Approximate Computing in General-Purpose Processors., , , and . DATE, page 481-486. IEEE, (2021)Layerwise Disaggregated Evaluation of Spiking Neural Networks., , , and . ISLPED, page 25:1-25:6. ACM, (2022)RaPiD: AI Accelerator for Ultra-low Precision Training and Inference., , , , , , , , , and 44 other author(s). ISCA, page 153-166. IEEE, (2021)A Case for Generalizable DNN Cost Models for Mobile Devices., , , , and . IISWC, page 169-180. IEEE, (2020)14.1 A Software-Assisted Peak Current Regulation Scheme to Improve Power-Limited Inference Performance in a 5nm AI SoC., , , , , , , , , and 36 other author(s). ISSCC, page 254-256. IEEE, (2024)Deep Compression of Pre-trained Transformer Models., , , , , , , and . NeurIPS, (2022)Efficiency attacks on spiking neural networks., , , , and . DAC, page 373-378. ACM, (2022)AxFormer: Accuracy-driven Approximation of Transformers for Faster, Smaller and more Accurate NLP Models., , , and . IJCNN, page 1-8. IEEE, (2022)