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@KULeuven-MICAS

MICAS (KU Leuven)

We initiate, drive and realize breakthroughs in micro and nano-electronic systems, for a better and more comfortable life for everyone.

This is the GitHub organization for the MICAS research group at KU Leuven. Here you can find open-source projects made by us and our collaborators.

SNAX Framework

Snax is an end-to-end, open-source multi-accelerator cluster design framework. It consists of the snax-mlir compile toolchain and the snax cluster hardware framework.

SNAX-MLIR Repo

SNAX Cluster Repo

HeMAiA Repo

Fast DNN Accelerator Design Space Exploration Frameworks

ZigZag targets rapid DSE for DNN accelerator platforms supporting an broad set of hardware architectures and workload scheduling scenarios beyond other existing frameworks.
The latest version of ZigZag includes support for modeling of analog and digital in-memory computing accelerators and estimating both peak/workload performance at the macro and system level.

Stream is an extension of ZigZag capable of modeling multi-core DNN acceleration employing fine-grained layer-fused processing.

ZigZag-LLM Repo

ZigZag-LLM is a framework to rapidly model Large Language Models on dedicated, single-core accelerators and facilitates early identification of energy bottlenecks within the hardware architecture.

DeFiNes Repo

DeFiNes extends ZigZag to enable the DSE of cross-layer depth-first scheduling (a.k.a. layer fusion, or cascaded execution)

Accelerator-aware Neural Network Deployment

HTVM Repo

HTVM is a neural network compiler based on Dory and TVM that allows for efficient neural network deployment on heterogenous TinyML platforms with scratchpad-memory accelerators.

Artificial Intelligence System on Chips (SoCs)

TinyVers Repo

V. Jain, S. Giraldo, J. D. Roose, L. Mei, B. Boons and M. Verhelst, "TinyVers: A Tiny Versatile System-on-Chip With State-Retentive eMRAM for ML Inference at the Extreme Edge," in IEEE Journal of Solid-State Circuits, doi: 10.1109/JSSC.2023.3236566.

V. Jain, S. Giraldo, J. D. Roose, B. Boons, L. Mei and M. Verhelst, "TinyVers: A 0.8-17 TOPS/W, 1.7 μW-20 mW, Tiny Versatile System-on-chip with State-Retentive eMRAM for Machine Learning Inference at the Extreme Edge," 2022 IEEE Symposium on VLSI Technology and Circuits (VLSI Technology and Circuits), Honolulu, HI, USA, 2022, pp. 20-21, doi: 10.1109/VLSITechnologyandCir46769.2022.9830409.

K. Ueyoshi et al., "DIANA: An End-to-End Energy-Efficient Digital and ANAlog Hybrid Neural Network SoC," 2022 IEEE International Solid- State Circuits Conference (ISSCC), San Francisco, CA, USA, 2022, pp. 1-3, doi: 10.1109/ISSCC42614.2022.9731716.

P. Houshmand et al., "DIANA: An End-to-End Hybrid DIgital and ANAlog Neural Network SoC for the Edge," in IEEE Journal of Solid-State Circuits, vol. 58, no. 1, pp. 203-215, Jan. 2023, doi: 10.1109/JSSC.2022.3214064.

DPU Repo

N. Shah, L. I. G. Olascoaga, S. Zhao, W. Meert and M. Verhelst, "DPU: DAG Processing Unit for Irregular Graphs With Precision-Scalable Posit Arithmetic in 28 nm," in IEEE Journal of Solid-State Circuits, vol. 57, no. 8, pp. 2586-2596, Aug. 2022, doi: 10.1109/JSSC.2021.3134897.

DPU-v2 Repo

NN. Shah, W. Meert and M. Verhelst, "DPU-v2: Energy-efficient execution of irregular directed acyclic graphs," 2022 55th IEEE/ACM International Symposium on Microarchitecture (MICRO), Chicago, IL, USA, 2022, pp. 1288-1307, doi: 10.1109/MICRO56248.2022.00090.



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  1. zigzag zigzag Public

    HW Architecture-Mapping Design Space Exploration Framework for Deep Learning Accelerators

    C++ 148 48

  2. stream stream Public

    Multi-core HW accelerator mapping optimization framework for layer-fused ML workloads.

    Python 53 24

  3. htvm htvm Public

    Efficient Neural Network Deployment on Heterogenous TinyML Platforms

    Python 14

  4. snax_cluster snax_cluster Public

    Forked from pulp-platform/snitch_cluster

    A heterogeneous accelerator-centric compute cluster

    SystemVerilog 15 12

  5. snax-mlir snax-mlir Public

    Driving Snax with MLIR

    Python 14 4

  6. zigzag-llm zigzag-llm Public

    Model LLM inference on single-core dataflow accelerators

    Python 10 2

Repositories

Showing 10 of 45 repositories
  • snax-mlir Public

    Driving Snax with MLIR

    KULeuven-MICAS/snax-mlir’s past year of commit activity
    Python 14 4 2 3 Updated May 20, 2025
  • snax_cluster Public Forked from pulp-platform/snitch_cluster

    A heterogeneous accelerator-centric compute cluster

    KULeuven-MICAS/snax_cluster’s past year of commit activity
    SystemVerilog 15 Apache-2.0 66 21 (3 issues need help) 5 Updated May 20, 2025
  • mlir-networks Public

    Collection of scripts to convert neural network models into usable MLIR for use in snax-mlir

    KULeuven-MICAS/mlir-networks’s past year of commit activity
    Python 0 0 0 0 Updated May 20, 2025
  • xdma_axi_adapter Public

    The AXI Adapter for the XDMA Project

    KULeuven-MICAS/xdma_axi_adapter’s past year of commit activity
    SystemVerilog 1 1 0 0 Updated May 19, 2025
  • HeMAiA Public Forked from pulp-platform/occamy

    The warm and cozy house for SNAX Cluster 🏠

    KULeuven-MICAS/HeMAiA’s past year of commit activity
    Tcl 1 Apache-2.0 19 4 2 Updated May 19, 2025
  • llvm-build Public

    Some prebuilt compiler toolchains for the SNAX ecosystem

    KULeuven-MICAS/llvm-build’s past year of commit activity
    Shell 0 0 0 0 Updated May 13, 2025
  • stream Public

    Multi-core HW accelerator mapping optimization framework for layer-fused ML workloads.

    KULeuven-MICAS/stream’s past year of commit activity
    Python 53 MIT 24 7 3 Updated Apr 26, 2025
  • frontend-mess Public
    KULeuven-MICAS/frontend-mess’s past year of commit activity
    Python 0 0 0 0 Updated Apr 25, 2025
  • zigzag Public

    HW Architecture-Mapping Design Space Exploration Framework for Deep Learning Accelerators

    KULeuven-MICAS/zigzag’s past year of commit activity
    C++ 148 MIT 48 10 1 Updated Apr 3, 2025
  • praxis Public

    snax + mlir + zigzag

    KULeuven-MICAS/praxis’s past year of commit activity
    Python 0 0 0 0 Updated Mar 26, 2025

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