We present an agile methodology based on the open source Chipyard framework used for designing and validating manufacturable and performant heterogeneous RISC-V SoCs within the constraints of 15-week semesters by classes composed primarily of undergraduate students. Chipyard integrates configurable, generator-based IP blocks and flows, including the modular VLSI flow, Hammer, developed over a decade of tapeouts in different technologies. Students iterate their custom RTL and AMS blocks through integration, verification, and place-and-route, then write full-stack applications, characterizing performance. One recent semester’s class chips in FinFET are described: COSMIC (FFT, convolution, DMA accelerators), MELLIS (sparse‑matrix, convolution, quantized transformer engines, near‑memory MAC), and SCμM‑V (low-power crystal-free transceiver, general-purpose AFE, on-chip power management, clock generation). For example, COSMIC reaches 1.25 GHz, accelerates compute 2-12× with energy savings, and runs live demos. New documentation and infrastructure, such as the new bring-up platform, Baremetal, make Chipyard even more accessible.

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.

In this paper, we demonstrate a prototype system for path independent local oscillator (LO) synchronization of a distributed beamformer in wireless ad hoc networks. The system contains a low power full duplex (FD) transceiver IC, a RF phase interpolator IC, and a CDMA encoder/decoder to realize a conjugate loop and synchronize the LO’s of two RF nodes. Both ICs were fabricated in 180nm CMOS technology. The FD transceiver IC consumes 69mW at 700MHz and the RF phase interpolator IC consumes 75mW at 1.4GHz. Using the low power ICs, we demonstrate a simple, lightweight, and robust methodology to synchronize two LO’s with an average phase precision of 2.1° and 94% maximum beamforming gain using RF only transmissions via a single antenna per node.

A critical requirement for safe autonomous navigation of a planetary rover is the ability to accurately estimate the traversability of the terrain. This work considers the problem of predicting the attitude and configuration angles of the platform from terrain representations that are often incomplete due to occlusions and sensor limitations. Using Gaussian Processes (GP) and exteroceptive data as training input, we can provide a continuous and complete representation of terrain traversability, with uncertainty in the output estimates. In this paper, we propose a novel method that focuses on exploiting the explicit correlation in vehicle attitude and configuration during operation by learning a kernel function from vehicle experience to perform GP regression. We provide an extensive experimental validation of the proposed method on a planetary rover. We show significant improvement in the accuracy of our estimation compared with results obtained using standard kernels (Squared Exponential and Neural Network), and compared to traversability estimation made over terrain models built using state-of-the-art GP techniques.

It is well recognized that many scientifically interesting sites on Mars are located in rough terrains. Therefore, to enable safe autonomous operation of a planetary rover during exploration, the ability to accurately estimate terrain traversability is critical. In particular, this estimate needs to account for terrain deformation, which significantly affects the vehicle attitude and configuration. This paper presents an approach to estimate vehicle configuration, as a measure of traversability, in deformable terrain by learning the correlation between exteroceptive and proprioceptive information in experiments. We first perform traversability estimation with rigid terrain assumptions, then correlate the output with experienced vehicle configuration and terrain deformation using a multi-task Gaussian Process (GP) framework. Experimental validation of the proposed approach was performed on a prototype planetary rover and the vehicle attitude and configuration estimate was compared with state-of-the-art techniques. We demonstrate the ability of the approach to accurately estimate traversability with uncertainty in deformable terrain.