Specialization meets Flexibility: a Heterogeneous Architecture for High-Efficiency, High-flexibility AR/VR Processing

Emerging AR-VR applications execute complex heterogeneous workloads, mixing Deep-Learning(DL) and Digital-Signal-Processing(DSP) tasks, on SoCs embedded in the frame of eyeglasses, with implied tight power and area constraints, especially in the case of AR. We propose ArchiMEDES, an open-source heterogeneous-SoC platform with a programmable cluster of RISC-V cores coupled with a configurable DNN engine (NEureka) targeting AR/VR workloads. ArchiMEDES features a low-overhead Heterogeneous Cluster Interconnect(HCI) to enable fast RISC-V/NEureka cooperation on a shared tightly coupled data memory (TCDM). We show post-layout results targeting 22nm technology; ArchiMEDES shows a peak combined performance of up to 1.19 TOPS and an efficiency of up to 10.6 TOPS/W. Hardware-Software cooperation in ArchiMEDES enables a 5.5× speedup in an AR-VR gaze tracking case study, compared to a non-cooperative single-RISC-V + Accelerator system.