E3HDC: Energy Efficient Encoding for Hyper-Dimensional Computing on Edge Devices

被引:0
|
作者
Roodsari, Mahboobe Sadeghipour [1 ]
Krautter, Jonas [1 ]
Meyers, Vincent [1 ]
Tahoori, Mehdi [1 ]
机构
[1] Karlsruhe Inst Technol KIT, Inst Comp Engn, Karlsruhe, Germany
来源
2024 34TH INTERNATIONAL CONFERENCE ON FIELD-PROGRAMMABLE LOGIC AND APPLICATIONS, FPL 2024 | 2024年
关键词
hyperdimensional computing; HDC; Internet of Things; IoT; low power; edge device; configurable design;
D O I
10.1109/FPL64840.2024.00045
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Hyper-Dimensional Computing (HDC) as a brain-inspired computational model for cognitive tasks is suitable for edge devices due to its hardware-friendly and fault-resistant computations. Despite this potential, HDC has a large memory footprint, resulting high power consumption. In this work, we propose a hardware-aware encoding where parameters are generated on-the-fly without any large memory block requirements. Moreover, the hardware mapping of the trained HDC model is optimized to make it suitable for resource-constraint edge devices. In this work we propose an end-to-end flow from HDC training to FPGA mapping. We demonstrate the efficiency of this method compared to other state-of-the-art HDC implementations in terms of hardware usage and power consumption.
引用
收藏
页码:274 / 280
页数:7
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