  Emerging Artificial Intelligence-enabled Internet-of-Things (AI-IoT)
System-on-a-Chip (SoC) for augmented reality, personalized healthcare, and
nano-robotics need to run many diverse tasks within a power envelope of a few
tens of mW over a wide range of operating conditions: compute-intensive but
strongly quantized Deep Neural Network (DNN) inference, as well as signal
processing and control requiring high-precision floating-point. We present
Marsellus, an all-digital heterogeneous SoC for AI-IoT end-nodes fabricated in
GlobalFoundries 22nm FDX that combines 1) a general-purpose cluster of 16
RISC-V Digital Signal Processing (DSP) cores attuned for the execution of a
diverse range of workloads exploiting 4-bit and 2-bit arithmetic extensions
(XpulpNN), combined with fused MAC&LOAD operations and floating-point support;
2) a 2-8bit Reconfigurable Binary Engine (RBE) to accelerate 3x3 and 1x1
(pointwise) convolutions in DNNs; 3) a set of On-Chip Monitoring (OCM) blocks
connected to an Adaptive Body Biasing (ABB) generator and a hardware control
loop, enabling on-the-fly adaptation of transistor threshold voltages.
Marsellus achieves up to 180 Gop/s or 3.32 Top/s/W on 2-bit precision
arithmetic in software, and up to 637 Gop/s or 12.4 Top/s/W on
hardware-accelerated DNN layers.

Hardware Architecture;Artificial Intelligence;Machine Learning