Abstract

Extended reality (XR) applications are machine learning (ML)-intensive, featuring deep neural networks (DNNs) with millions of weights, tightly latency-bound (10-20 ms end-to-end), and power-constrained (low tens of mW average power). While ML performance and efficiency can be achieved by introducing neural engines within low-power systems-on-chip (SoCs), system-level power for nontrivial DNNs depends strongly on the energy of non-volatile memory (NVM) access for network weights. This work introduces Siracusa, a near-sensor heterogeneous SoC for next-generation XR devices manufactured in 16 nm CMOS. Siracusa couples an octa-core cluster of RISC-V digital signal processing (DSP) cores with a novel tightly coupled "At-Memory" integration between a state-of-the-art digital neural engine called and an on-chip NVM based on magnetoresistive random access memory (MRAM), achieving 1.7x higher throughput and 3x better energy efficiency than XR SoCs using NVM as background memory. The fabricated SoC prototype achieves an area efficiency of 65.2 GOp/s/mm(2) and a peak energy efficiency of 8.84 TOp/J for DNN inference while supporting complex, heterogeneous application workloads, which combine ML with conventional signal processing and control.