The computing industry is forced to find alternative design approaches and computing platforms to sustain increased power efficiency, while providing sufficient performance. Among the examined solutions, Approximate Computing, Hardware Acceleration, and Heterogeneous Computing have gained great momentum. In this Dissertation, we introduce design solutions and methodologies, built on top of the preceding computing paradigms, for the development of energy-efficient DSP and AI accelerators. In particular, we adopt the promising paradigm of Approximate Computing and apply new approximation techniques in the design of arithmetic circuits. The proposed arithmetic approximation techniques involve bit-level optimizations, inexact operand encodings, and skipping of computations, while they are applied in both fixed- and floating-point arithmetic. We also conduct an extensive exploration on combinations among the approximation techniques and propose a low-overhead scheme for seamlessly adjusting the approximation degree of our circuits at runtime. Based on our methodology, these arithmetic approximation techniques are then combined with hardware design techniques to implement approximate ASIC- and FPGA-based DSP and AI accelerators. Moreover, we propose methodologies for the efficient mapping of DSP/AI kernels on distinctive embedded devices, i.e., the space-grade FPGAs and the heterogeneous VPUs. On the one hand, we cope with the decreased flexibility of the space-grade technology and the technical challenges that arise in new FPGA tools. On the other hand, we unlock the full potential of heterogeneity by exploiting all the diverse processors and memories. Based on our methodology, we efficiently map computer vision algorithms onto the radiation-hardened NanoXplore's FPGAs and accelerate DSP & CNN kernels on Intel's Myriad VPUs.

翻译：计算行业被迫寻求替代设计方法和计算平台，以在提供足够性能的同时维持能效提升。在所研究的解决方案中，近似计算、硬件加速和异构计算已获得显著发展。本论文基于前述计算范式，提出用于开发高能效DSP与AI加速器的设计方案和方法论。具体而言，我们采用前景广阔的近似计算范式，在算术电路设计中应用新型近似技术。所提出的算术近似技术涉及位级优化、非精确操作数编码和计算跳过，并同时应用于定点与浮点算术。我们还对近似技术组合进行了广泛探索，提出一种低开销方案，可在运行时无缝调整电路近似度。基于该方法，这些算术近似技术进一步与硬件设计技术结合，实现基于ASIC和FPGA的近似DSP与AI加速器。此外，我们提出了面向特殊嵌入式设备（即航天级FPGA和异构VPU）的DSP/AI内核高效映射方法论。一方面，我们应对航天级技术灵活性降低以及新型FPGA工具带来的技术挑战；另一方面，通过充分利用所有异构处理器和存储器，释放异构性的全部潜力。基于该方法，我们成功将计算机视觉算法高效映射至耐辐射NanoXplore FPGA，并在Intel Myriad VPU上加速DSP与CNN内核。