Processing in memory (PIM) moves computation into memories with the goal of improving throughput and energy-efficiency compared to traditional von Neumann-based architectures. Most existing PIM architectures are either general-purpose but only support atomistic operations, or are specialized to accelerate a single task. We propose the Parallel Processor in Associative Content-addressable memory (PPAC), a novel in-memory accelerator that supports a range of matrix-vector-product (MVP)-like operations that find use in traditional and emerging applications. PPAC is, for example, able to accelerate low-precision neural networks, exact/approximate hash lookups, cryptography, and forward error correction. The fully-digital nature of PPAC enables its implementation with standard-cell-based CMOS, which facilitates automated design and portability among technology nodes. To demonstrate the efficacy of PPAC, we provide post-layout implementation results in 28nm CMOS for different array sizes. A comparison with recent digital and mixed-signal PIM accelerators reveals that PPAC is competitive in terms of throughput and energy-efficiency, while accelerating a wide range of applications and simplifying development.
点云上的深度学习在自动驾驶和AR/VR等广泛应用中发挥着至关重要的作用。这些应用程序在边缘设备上与人实时交互，因此需要低延迟和低能耗。与将点云投影到2D空间相比，直接处理3D点云可获得更高的精度和更低的MAC数。然而，点云的极稀疏性质对硬件加速提出了挑战。例如，我们需要显式地确定非零输出并搜索非零邻居（映射操作），这在现有加速器中是不支持的。此外，需要稀疏特征的显式聚集和分散，从而导致大的数据移动开销。本文全面分析了现代点云网络在CPU/GPU/TPU上的性能瓶颈。为了解决这些挑战，我们提出了PointAcc，一种新颖的点云深度学习加速器。PointAcc将不同的映射操作映射到一个通用的基于排名的内核上，通过可配置的缓存来流式传输稀疏计算，并在时间上融合连续的密集层以减少内存占用。在4个应用程序的8个点云模型上进行评估，PointAcc比RTX 2080 Ti GPU实现了3.7倍的加速和22倍的节能。PointAcc与轻量级神经网络共同设计，在S3 DIS数据集上运行分割的准确性提高了9.1%，速度提高了100倍，可与之前的加速器Mesorasi相媲美。PointAcc为高效的点云识别铺平了道路。
