Convolutional neural networks (CNNs) are emerging as powerful tools for visual recognition. Recent architecture proposals for sparse CNNs exploit zeros in the feature maps and filters for performance and energy without losing accuracy. Sparse architectures that exploit two-sided sparsity in both feature maps and filters have been studied only at small scales (e.g., 1K multiply-accumulate(MAC) units). However, to realize their advantages in full, the sparse architectures have to be scaled up to levels of the dense architectures (e.g., 32K MACs in the TPU). Such scaling is challenging since achieving reuse through broadcasts incurs implicit barrier cost raises the inter-related issues of load imbalance, buffering, and on-chip bandwidth demand. SparTen, a previous scheme, addresses one aspect of load balancing but not other aspects, nor the other issues of buffering and bandwidth. To that end, we propose the barrier-free large-scale sparse tensor accelerator (BARISTA). BARISTA (1) is the first architecture for scaling up sparse CNN accelerators; (2) reduces on-chip bandwidth demand by telescoping request-combining the input map requests and snarfing the filter requests; (3) reduces buffering via basic buffer sharing and avoids the ensuing barriers between consecutive input maps by coloring the output buffers; (4) load balances intra-filter work via dynamic round-robin work assignment; and (5) employs hierarchical buffering which achieves high cache bandwidth via a few, wide, shared buffers and low buffering via narrower, private buffers at the compute. Our simulations show that, on average, barista performs 5.4x, 2.2x, 1.7x, 2.5x better than a dense, a one-sided, a naively-scaled two-sided, and an iso-area two-sided architecture, respectively. Using 45-nm technology, ASIC synthesis of our RTL design for four clusters of 8K MACs at 1 GHz clock speed, reports 213 mm$^2$ area and 170 W power.

翻译：光学神经网络(CNNs)正在成为强大的视觉识别工具。 最近为稀有CNN提供的架构提案在功能地图和过滤器中开发零,用于性能和能量,而不会失去准确性。在功能地图和过滤器中开发双向宽度的松散架构只在小规模研究(例如,1K乘积(MAC)单位 ) 。然而,为了全面实现其优势,稀薄的架构必须扩大至密集结构的水平(例如,平流层中,32K MICs ) 。这种扩大具有挑战性,因为通过广播实现再利用,而导致的缓冲成本成本成本更高。 SparTen, 先前的一个方案, 处理的是负载平衡的一个方面, 而不是其它方面, 以及缓冲和带宽的其他问题。 为此, 我们提议将大型的无屏障结构(比如) 平流压软压, 平流压的螺旋加速度(BARISTA) (1) 是第一个在不断递增的CNNC 直径直径直径直径直径直径直径直径直径直路路路路路路路路路路的服务器报告的架构;(2) 平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平