TPE:具有多层次转换机制的高性能边缘器件推理

2023 IEEE 5th International Conference on Artificial Intelligence Circuits and Systems (AICAS) Pub Date : 2023-06-11 DOI:10.1109/AICAS57966.2023.10168614

Zhou Wang, Jingchuang Wei, Xiaonan Tang, Boxiao Han, Hongjun He, Leibo Liu, Shaojun Wei, S. Yin

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引用次数: 0

摘要

长期以来，边缘设备的深度神经网络推理一直是计算量大、能耗大的重要问题。本文提出了一种具有三个特征的TPE(Transformation Process Element)。首先，TPE具有数据分割跳过和预重组(DSSPR)方法。其次，TPE具有典型的数值匹配与校准计算机(TVMCC)系统，将直接计算转换为匹配与校准计算。第三，TPE包含数据格式预配置和自调整(DFPCSA)方案。与最典型的纯推理处理器UNPU相比，TPE的能耗提高了1.25倍。

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TPE: A High-Performance Edge-Device Inference with Multi-level Transformational Mechanism

DNN inference of edge devices has been very important for a long time with large computing and energy consumption demand. This paper proposes a TPE(Transformation Process Element) with three characteristics. Firstly, TPE has a method of Data Segmentation Skip and Pre-Reorganization(DSSPR). Secondly, TPE has a Typical Value Matching and Calibration Computer (TVMCC) system, which converts direct calculation into matching and calibration calculation. Thirdly, TPE includes a Data Format Pre-Configuration and Self-Adjustment (DFPCSA) scheme. Compared with the most typical pure reasoning processor UNPU, TPE achieves 1.25× better energy consumption.

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2023 IEEE 5th International Conference on Artificial Intelligence Circuits and Systems (AICAS)

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