用于视觉应用的传输触发阵列处理器
Transport Triggered Array Processor for Vision Applications
摘要：
许多物联网（IoT）设备中的低级传感数据处理通过利用睡眠模式或将时钟减慢到最低限度来追求能效。为了抑制这些设计中的待机功耗份额，在制造中采用了近阈值/亚阈值操作点或超低泄漏工艺。这些限制了时钟频率，降低了单个处理核心的计算吞吐量。在这篇文章中，我们探索通过大规模并行化来补偿在近阈值区域（Δ =0.6V）操作的性能损失。近阈值操作和大规模并行的好处分别是每个指令操作的最佳能耗和最小化的内存往返。该设计的处理元件（PE）基于传输触发架构。细粒度的可编程并行解决方案允许快速有效地计算可学习的低级特征（例如局部二进制描述符和卷积）。还实施了其他业务，包括最大池。可编程设计实现了本地二进制模式计算的出色能效。
英文摘要：
Low-level sensory data processing in many Internet-of-Things (IoT) devices pursue energy efficiency by utilizing sleep modes or slowing the clocking to the minimum. To curb the share of stand-by power dissipation in those designs, near-threshold/sub-threshold operational points or ultra-low-leakage processes in fabrication are employed. Those limit the clocking rates significantly, reducing the computing throughputs of individual processing cores. In this contribution we explore compensating for the performance loss of operating in near-threshold region (Vdd =0.6V) through massive parallelization. Benefits of near-threshold operation and massive parallelism are optimum energy consumption per instruction operation and minimized memory roundtrips, respectively. The Processing Elements (PE) of the design are based on Transport Triggered Architecture. The fine grained programmable parallel solution allows for fast and efficient computation of learnable low-level features (e.g. local binary descriptors and convolutions). Other operations, including Max-pooling have also been implemented. The programmable design achieves excellent energy efficiency for Local Binary Patterns computations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
