ucb-bar/gemmini
Some data may be outdated — see provenance labels.
Berkeley's Spatial Array Generator observed · 2026-10-02
Health v2 · maintenance only
66/100
- Activity 97
- Release rhythm 8
- Longevity 100
Flags: no_license
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-10-21. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 2912
- days_rel: n/a
- days_push: 19
- n_releases_24m: 0
Adoption not part of the score
1493 stars · 296 forks observed · 2026-10-02
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
Gemmini is Berkeley's open-source generator for parameterizable systolic-array DNN hardware accelerators, written in Chisel (Scala) and integrated with the Chipyard SoC design ecosystem. It provides a full-system, full-stack platform for exploring and evaluating how hardware and software co-design choices affect deep neural network performance, including simulators and software toolchains.
Use cases
- generate a custom systolic array DNN accelerator for RISC-V SoCs
- evaluate DNN accelerator performance in cycle-accurate simulation
- explore hardware-software co-design tradeoffs for neural network accelerators
- run ResNet50 and other DNN models on a simulated custom accelerator
- prototype an ASIC or FPGA neural network accelerator
- study how memory hierarchies and software stacks affect DNN accelerator performance
When to choose
- you are a computer architect researching DNN accelerator designs
- you need a parameterizable systolic array generator integrated with RISC-V SoCs via Chipyard
- you want full-system simulation of custom accelerators with real DNN workloads
- you are building an academic or research prototype of a matrix-multiplication accelerator
When to avoid
- you need a production-ready commercial accelerator or driver stack
- you want to accelerate DNNs on existing GPUs or CPUs without hardware design work
- you are unfamiliar with Chisel, RISC-V, and hardware simulation flows
- you need a quick plug-and-play inference solution
Facets
framework · maturity active
simulation machine-learning deep-learning compiler developer-tools hardware machine-learning deep-learning simulation python cpp jvm hardware-accelerator chisel risc-v asic dnn systolic-array chipyard fpga hardware-design research linux
1 source
- readme: https://github.com/ucb-bar/gemmini · fetched 2026-10-02 · 39b0a8479dd5
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| ucb-bar/gemmini | main | 66 |
For agents
Data as of 2026-10-19T08:05:51.212177+00:00 · Report a problem