Pradip Bose, Augusto Vega, et al.
HPCA 2025
Heterogeneous systems-on-chips (SoCs) are pivotal for real-time applications like autonomous driving, as they blend the versatility of CPUs with the efficiency of accelerator IPs. However, evolving application demands necessitate domain-specific SoCs to meet real-time deadlines within strict power and area constraints. While prior research focused on microarchitectural optimizations, overlooking broader system-level considerations can lead to suboptimal design decisions. Thus, there is a need to elevate the abstraction level of design space exploration (DSE) to the SoC level. However, SoC-level DSE is challenging due to the vast design space, encompassing microarchitectural parameters and dynamic task-to-hardware mapping choices based on runtime characteristics and real-time constraints. This paper proposes a systematic and agile methodology, called ARTEMIS, for efficient DSE of real-time, domain-specific SoCs that are constrained by task deadlines, power, and area. The core concept involves integrating a dynamic SoC scheduler to reduce the design space by eliminating the mapping dimension. Enhanced scheduling policies, incorporating techniques like task procrastination and memory-traffic/energy awareness, expedite navigation through the pruned design space. Additionally, DSE heuristics are optimized with real-time deadline and power/areaaware ranking mechanisms. ARTEMIS is evaluated on autonomous vehicle (AV) and augmented/virtual reality (AR/VR) applications, and additionally validated on an FPGA. Compared to the state-of-the-art, DSE using ARTEMIS converges 5.112.8 × faster, while yielding SoCs that meet 100% real-time deadlines with 1.2-3 × better throughput at iso-area or up to 2.4 × lower area for at iso-input-rate. ARTEMIS thus enables DSE of large designs with tractable simulation resources, without compromising on the power-performance-area metrics of the explored SoC design.
Pradip Bose, Augusto Vega, et al.
HPCA 2025
Kaustabha Ray
IEEE TNSM
Andrew S. Cassidy, John V. Arthur, et al.
ISSCC 2024
Arun Paidimarri, Asaf Tzadok, et al.
GOMACTech 2024