一句话：提出 robomorphic computing，将机器人形态转化为定制加速器结构，并自动生成运动规划内核的高效硬件设计。
机器人应用时延约束严格、计算负担重，运动规划与控制中执行器响应速率与先进软件方案之间至少相差一个数量级。本文提出 robomorphic computing：把机器人拓扑与结构映射为定制加速器形态，利用并行性和矩阵稀疏模式，自动生成参数化硬件设计。作者以刚体动力学梯度这一关键内核为例，在工业机械臂上评估 FPGA 与 ASIC 实现。FPGA 单次动力学梯度计算相比 CPU/GPU 加速 8× 和 86×；作为协处理器并计入 I/O 往返延迟处理多次计算时仍保持 1.9×–2.9× 加速，ASIC 综合显示单次计算时延可再提速 7.2×。该方法可推广到四足、人形机器人及其他机器人内核。
Robotics applications have hard time constraints and heavy computational burdens that can greatly benefit from domain-specific hardware accelerators. For the latency-critical problem of robot motion planning and control, there exists a performance gap of at least an order of magnitude between joint actuator response rates and state-of-the-art software solutions. Hardware acceleration can close this gap, but it is essential to define automated hardware design flows to keep the design process agile as applications and robot platforms evolve. To address this challenge, we introduce robomorphic computing: a methodology to transform robot morphology into a customized hardware accelerator morphology. We (i) present this design methodology, using robot topology and structure to exploit parallelism and matrix sparsity patterns in accelerator hardware; (ii) use the methodology to generate a parameterized accelerator design for the gradient of rigid body dynamics, a key kernel in motion planning; (iii) evaluate FPGA and synthesized ASIC implementations of this accelerator for an industrial manipulator robot; and (iv) describe how the design can be automatically customized for other robot models. Our FPGA accelerator achieves speedups of 8× and 86× over CPU and GPU when executing a single dynamics gradient computation. It maintains speedups of 1.9× to 2.9× over CPU and GPU, including computation and I/O round-trip latency, when deployed as a coprocessor to a host CPU for processing multiple dynamics gradient computations. ASIC synthesis indicates an additional 7.2× speedup for single computation latency. We describe how this principled approach generalizes to more complex robot platforms, such as quadrupeds and humanoids, as well as to other computational kernels in robotics, outlining a path forward for future robomorphic computing accelerators.
@inproceedings{paperbot6137,
title = {Robomorphic computing: a design methodology for domain-specific accelerators parameterized by robot morphology},
author = {Sabrina M. Neuman and Brian Plancher and Thomas Bourgeat and Thierry Tambe and Srinivas Devadas and Vijay Janapa Reddi},
booktitle = {ASPLOS},
year = {2021},
doi = {10.1145/3445814.3446746}
}