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RaPiD: AI Accelerator for Ultra-low Precision Training and Inference., , , , , , , , , and 44 other author(s). ISCA, page 153-166. IEEE, (2021)ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training., , , , , , , , , and 1 other author(s). NeurIPS, (2020)A 3.0 TFLOPS 0.62V Scalable Processor Core for High Compute Utilization AI Training and Inference., , , , , , , , , and 33 other author(s). VLSI Circuits, page 1-2. IEEE, (2020)Innovate Practices on CyberSecurity of Hardware Semiconductor Devices., , , , , , , , , and 4 other author(s). VTS, page 1. IEEE, (2019)4-Bit Quantization of LSTM-Based Speech Recognition Models., , , , , , , , , and 2 other author(s). Interspeech, page 2586-2590. ISCA, (2021)Accumulation Bit-Width Scaling For Ultra-Low Precision Training Of Deep Networks., , , , , , and . ICLR (Poster), OpenReview.net, (2019)Novel IC Sub-Threshold IDDQ Signature And Its Relationship To Aging During High Voltage Stress., , and . ESSDERC, page 250-253. IEEE, (2018)Training Deep Neural Networks with 8-bit Floating Point Numbers., , , , and . NeurIPS, page 7686-7695. (2018)Across the Stack Opportunities for Deep Learning Acceleration., , , , , , , , , and 21 other author(s). ISLPED, page 35:1-35:2. ACM, (2018)Mitigating the Impact of Outlier Channels for Language Model Quantization with Activation Regularization., , , , , and . CoRR, (2024)