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Efficient Winograd or Cook-Toom Convolution Kernel Implementation on Widely Used Mobile CPUs.

, , , , , and . EMC2@HPCA/CVPR/ISCA, page 1-5. IEEE, (2019)

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Learning Low-precision Neural Networks without Straight-Through Estimator (STE)., and . IJCAI, page 3066-3072. ijcai.org, (2019)FixyNN: Efficient Hardware for Mobile Computer Vision via Transfer Learning., , , , , and . CoRR, (2019)High Throughput Matrix-Matrix Multiplication between Asymmetric Bit-Width Operands., , and . CoRR, (2020)Compressing RNNs to Kilobyte Budget for IoT Devices Using Kronecker Products., , , , , , and . ACM J. Emerg. Technol. Comput. Syst., 17 (4): 46:1-46:18 (2021)Compressing RNNs for IoT devices by 15-38x using Kronecker Products., , , , , , and . CoRR, (2019)S2TA: Exploiting Structured Sparsity for Energy-Efficient Mobile CNN Acceleration., , , and . HPCA, page 573-586. IEEE, (2022)Debiasing Model Updates for Improving Personalized Federated Training., , , , , , and . ICML, volume 139 of Proceedings of Machine Learning Research, page 21-31. PMLR, (2021)Searching for Winograd-aware Quantized Networks., , , and . MLSys, mlsys.org, (2020)TinyLSTMs: Efficient Neural Speech Enhancement for Hearing Aids., , , , , , , and . INTERSPEECH, page 4054-4058. ISCA, (2020)Mobile Machine Learning Hardware at ARM: A Systems-on-Chip (SoC) Perspective., , and . CoRR, (2018)