  The Internet-of-Things requires end-nodes with ultra-low-power always-on
capability for a long battery lifetime, as well as high performance, energy
efficiency, and extreme flexibility to deal with complex and fast-evolving
near-sensor analytics algorithms (NSAAs). We present Vega, an IoT end-node SoC
capable of scaling from a 1.7 $\mathrm{\mu}$W fully retentive cognitive sleep
mode up to 32.2 GOPS (@ 49.4 mW) peak performance on NSAAs, including mobile
DNN inference, exploiting 1.6 MB of state-retentive SRAM, and 4 MB of
non-volatile MRAM. To meet the performance and flexibility requirements of
NSAAs, the SoC features 10 RISC-V cores: one core for SoC and IO management and
a 9-cores cluster supporting multi-precision SIMD integer and floating-point
computation. Vega achieves SoA-leading efficiency of 615 GOPS/W on 8-bit INT
computation (boosted to 1.3TOPS/W for 8-bit DNN inference with hardware
acceleration). On floating-point (FP) compuation, it achieves SoA-leading
efficiency of 79 and 129 GFLOPS/W on 32- and 16-bit FP, respectively. Two
programmable machine-learning (ML) accelerators boost energy efficiency in
cognitive sleep and active states, respectively.
