2021
DOI: 10.1007/978-3-030-80568-5_20
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Squeeze-and-Threshold Based Quantization for Low-Precision Neural Networks

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Cited by 3 publications
(1 citation statement)
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“…This has multiple reasons, including the demand for more privacy, lower latency, and stand-alone solutions that do not rely on a network connection or remote datacenter. This shift was enabled by the increasing compute performance and efficiency of mobile and embedded hardware, as well as novel hardware developments, such as new vector-extensions [1] and dedicated AI accelerators [1][2][3], but also new software developments such as kernel libraries [4,5] and dedicated frameworks [6][7][8], as well as optimization techniques, such as quantization [9,10] and pruning [11].…”
Section: Introductionmentioning
confidence: 99%
“…This has multiple reasons, including the demand for more privacy, lower latency, and stand-alone solutions that do not rely on a network connection or remote datacenter. This shift was enabled by the increasing compute performance and efficiency of mobile and embedded hardware, as well as novel hardware developments, such as new vector-extensions [1] and dedicated AI accelerators [1][2][3], but also new software developments such as kernel libraries [4,5] and dedicated frameworks [6][7][8], as well as optimization techniques, such as quantization [9,10] and pruning [11].…”
Section: Introductionmentioning
confidence: 99%