2020
DOI: 10.4218/etrij.2020-0134
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AB9: A neural processor for inference acceleration

Abstract: We present AB9, a neural processor for inference acceleration. AB9 consists of a systolic tensor core (STC) neural network accelerator designed to accelerate artificial intelligence applications by exploiting the data reuse and parallelism characteristics inherent in neural networks while providing fast access to large on‐chip memory. Complementing the hardware is an intuitive and user‐friendly development environment that includes a simulator and an implementation flow that provides a high degree of programma… Show more

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Cited by 8 publications
(4 citation statements)
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“…P dynamic is proportional to α, C, f , and V 2 , as expressed in (2), which correspond to the switching activity, the switching capacitance, the operating frequency, and the square of operating voltage, respectively. Among them, f V 2 is determined by PLLs and on-board PMICs, regardless of operations.…”
Section: Operation-aware Power Modelingmentioning
confidence: 99%
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“…P dynamic is proportional to α, C, f , and V 2 , as expressed in (2), which correspond to the switching activity, the switching capacitance, the operating frequency, and the square of operating voltage, respectively. Among them, f V 2 is determined by PLLs and on-board PMICs, regardless of operations.…”
Section: Operation-aware Power Modelingmentioning
confidence: 99%
“…Our proposed DFS applies the highest f allowed under the power limit of an NPU before processing each layer. To determine the highest allowable f, we build a power model of an NPU chip (i.e., AB9 [2]) to predict the power consumption of an NPU based on a real measurement; we measure power values for hardware resources such as processing units. Based on the prediction from the power model, our proposed DFS adjusts f as high as possible without violating the power limit.…”
Section: Introductionmentioning
confidence: 99%
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“…We introduced AB9, an AI processor including a quadcore system-on-chip (SoC) and a systolic tensor core (STC) for the inference acceleration [1]- [3]. The overall system is illustrated in Fig.…”
Section: System Overviewmentioning
confidence: 99%