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Performance vs. hardware requirements in state-of-the-art automatic speech recognition.

, , , and . EURASIP J. Audio Speech Music. Process., 2021 (1): 28 (2021)

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A2Q: Accumulator-Aware Quantization with Guaranteed Overflow Avoidance., , and . ICCV, page 16943-16952. IEEE, (2023)Performance vs. hardware requirements in state-of-the-art automatic speech recognition., , , and . EURASIP J. Audio Speech Music. Process., 2021 (1): 28 (2021)A2Q+: Improving Accumulator-Aware Weight Quantization., , , and . ICML, OpenReview.net, (2024)Soluzioni Infrastrutturali Open Source per il Sistema Pubblico di Cooperazione Applicativa., , and . SEBD, page 156-167. (2008)On hybrid genetic models for hard problems., , , and . FUZZ-IEEE, page 2142-2147. IEEE, (2009)Mix-GEMM: An efficient HW-SW Architecture for Mixed-Precision Quantized Deep Neural Networks Inference on Edge Devices., , , , , , and . HPCA, page 1085-1098. IEEE, (2023)Monitoring and Adaptation in Smart Spaces for Disabled Children., , , , , and . AVI, page 224-227. ACM, (2016)The Spicy system: towards a notion of mapping quality., , , , and . SIGMOD Conference, page 1289-1294. ACM, (2008)Using Brain Signals in Adaptive Smart Spaces for Disabled Children., , , , , and . CHI Extended Abstracts, page 1684-1690. ACM, (2016)Trainable Preprocessing for Reduced Precision Neural Networks., , , , and . EUSIPCO, page 1546-1550. IEEE, (2021)