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A 178-MSs Compressed Sensing Radar Accelerator Using a Spiking Neural Network Pe
一种基于脉冲神经网络的压缩感知雷达加速器，显著提升目标距离和速度估计精度
200,000次场景重建/秒，精度提升6倍，吞吐量提升8倍，能效提升18倍
压缩感知脉冲神经网络雷达处理器基追踪去噪硬件加速
▸使用生物启发的脉冲神经网络求解基追踪去噪问题
▸独特的权重压缩技术实现片上存储全连接网络权重
▸高吞吐量实时场景重建能力
Abstract
A prototype compressed sensing radar processor
boosts the accuracy of target range and velocity estimations
by over 6 × compared with conventional processing techniques.
The prototype numerically solves basis pursuit denoising with a
biologically plausible spiking neural network. A unique form
of weight compression allows on-chip storage of all weights
for the large fully connected network. Capable of producing
over 200 000 range–velocity scene reconstructions per second,
the prototype improves