This paper introduces NeCTAr (Near-Cache Transformer Accelerator), a 16nm heterogeneous multicore RISC-V SoC for sparse and dense machine learning kernels with both near-core and near-memory accelerators. A prototype chip runs at 400MHz at 0.85V and performs matrix-vector multiplications with 109 GOPs/W. The effectiveness of the design is demonstrated by running inference on a sparse language model, ReLU-Llama.

OsciBear is a system-on-chip (SoC) featuring a RISC-V 32-bit 5-stage in-order scalar processor, AES accelerator, BLE 1M baseband-modem, and a 2.4 GHz radio front end (RFE) transceiver. It was designed in TSMC's 28nm process with a total die area of 1 mm2during the course of a 14-week semester by 18 students - 4 Ph.D students, 6 masters students, and 8 undergraduates - enrolled in UC Berkeley's special topics course “28nm SoC for loT” in Spring 2021. Additionally, a PCB was designed with off-chip reference clocks, bring-up tooling, as well as power amplifiers, RF switch, and an antenna to complete the radio front-end. The CPU has been demonstrated to run up to 30 MHz in typical operating conditions. The BLE 1M-compliant PHY layer packet assembly and disassembly has been verified in-hardware through “loopback” testing. Adherence to BLE's PHY FM specifications has also been verified with a commercial BLE receiver. In total, the chip consumes 8.43 mW of static power.

We present a reinforcement learning (RL) framework that enables quadrupedal robots to perform soccer goalkeeping tasks in the real world. Soccer goalkeeping with quadrupeds is a challenging problem, that combines highly dynamic locomotion with precise and fast non-prehensile object (ball) manipulation. The robot needs to react to and intercept a potentially flying ball using dynamic locomotion maneuvers in a very short amount of time, usually less than one second. In this paper, we propose to address this problem using a hierarchical model-free RL framework. The first component of the framework contains multiple control policies for distinct locomotion skills, which can be used to cover different regions of the goal. Each control policy enables the robot to track random parametric end-effector trajectories while performing one specific locomotion skill, such as jump, dive, and sidestep. These skills are then utilized by the second part of the framework which is a high-level planner to determine a desired skill and end-effector trajectory in order to intercept a ball flying to different regions of the goal. We deploy the proposed framework on a Mini Cheetah quadrupedal robot and demonstrate the effectiveness of our framework for various agile interceptions of a fast-moving ball in the real world.