FATE: Boosting the Performance of Hyper-Dimensional Computing Intelligence with Flexible Numerical DAta TypE

Hyper-Dimensional Computing (HDC) is a promising braininspired learning framework designed for efficient, hardwarefriendly computation.By utilizing highly parallel operations, HDC encodes raw data into a hyper-dimensional space, facilitating efficient training and inference processes.However, the high precision required for representing high-dimensional vectors presents challenges for implementing HDC on resource-constrained edge devices, mainly due to the significant computational cost of performing multiplication for cosine similarity calculations.On the other hand, binary HDC offers lower costs but sacrifices accuracy.This paper addresses these challenges by focusing on data quantization, a hardware-efficient compression technique that incorporates sparsity to effectively balance cost and accuracy on embedded FPGA.Unlike existing methods that employ the same quantization scheme for all dimensions, we propose a novel solution that applies different numerical data types to different dimensions of data representations.This approach is motivated by two factors: * Both authors contributed equally to the paper.