论文标题
PISA：用于边缘图像处理的二进制加速器加速器
PISA: A Binary-Weight Processing-In-Sensor Accelerator for Edge Image Processing
论文作者
论文摘要
这项工作提出了一种传感器加速器的处理，即PISA，作为AI设备中实时和智能图像处理的灵活，节能和高性能解决方案。 PISA本质地在传感器侧利用具有非挥发性重量的新型计算像素来实现双向重量神经网络（BWNNS）中的粗粒卷积操作。这显着降低了数据转换和传输到外芯片处理器的功耗。该设计以近距离传播的DRAM计算单元进行处理，以处理其余的网络层。一旦检测到对象，PISA便将仅使用近传感器处理单元切换到典型的传感模式以捕获细粒度卷积的图像。与基线BWNN模型相比，我们对BWNN加速度的电路对构图的结果在各种图像数据集中表现出可接受的准确性，而PISA的帧速率为1000，效率为〜1.74 TOP/S/W。最后，与基线CPU传感器设计相比，PISA实质上将数据转换和传输能量降低了约84％。
This work proposes a Processing-In-Sensor Accelerator, namely PISA, as a flexible, energy-efficient, and high-performance solution for real-time and smart image processing in AI devices. PISA intrinsically implements a coarse-grained convolution operation in Binarized-Weight Neural Networks (BWNNs) leveraging a novel compute-pixel with non-volatile weight storage at the sensor side. This remarkably reduces the power consumption of data conversion and transmission to an off-chip processor. The design is completed with a bit-wise near-sensor processing-in-DRAM computing unit to process the remaining network layers. Once the object is detected, PISA switches to typical sensing mode to capture the image for a fine-grained convolution using only the near-sensor processing unit. Our circuit-to-application co-simulation results on a BWNN acceleration demonstrate acceptable accuracy on various image datasets in coarse-grained evaluation compared to baseline BWNN models, while PISA achieves a frame rate of 1000 and efficiency of ~1.74 TOp/s/W. Lastly, PISA substantially reduces data conversion and transmission energy by ~84% compared to a baseline CPU-sensor design.
