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NEureka - The Neural Network Accelerator of ArchiMEDES

NEureka is a Deep Neural Network accelerator which exploits the Hardware Processing Engine (HWPE) paradigm [1] (https://hwpe-doc.rtfd.io) and is designed to be integrated in an open-source PULP cluster configuration in combination with the Heterogeneous Cluster Interconnect (HCI). It makes use of the open-source IPs 'hci', 'hwpe-ctrl', and 'hwpe-stream'.

In general NEureka has built-in HW supports the following features:

  • Filters: 1x1, 3x3, depthwise
  • Batch normalization
  • ReLU
  • Activation input bits: 8
  • Weight bits: 2,3,4,5,6,7,8
  • Activation output bits: 8,32
  • Nr of input channels: arbitrary
  • Nr of output channels: arbitrary

NEureka is a direct derivative of the NE16 design https://github.com/pulp-platform/ne16 .

Simulating

Building the hardware simulation environment

The simulation infrastructure of NEureka uses QuestaSim. To build the environment, you can run the following with QuestaSim in your PATH:

# fetch Bender (if not available), update the dependencies, and generate the scripts
make update-ips
# build the simulation environment
make hw-all

Setting up the software environment

This version of NEureka relies on the https://github.com/pulp-platform/pulp-nnx library to generate simulation stimuli. You can fetch it in the model/deps folder as a submodule:

git submodule update --init

The pulp-nnx library has several Python requirements, such as PyTorch. Refer to model/requirements.txt for a list; you can install a Python VirtualEnv by running the create_venv.sh script and then entering the created environment:

source create_venv.sh
source venv/bin/activate

You also need a RISC-V GCC toolchain, i.e., riscv32-unknown-elf-gcc must be in your PATH.

Generating stimuli and running the simulation

You can generate stimuli with

make stimuli

To build the software generated test,

make sw-all

Finally, to run it:

# without QuestaSim GUI
make run
# with GUI
make run gui=1

There are several controllable parameters, such as filter size FS (1 or 3), input spatial size H_IN and W_IN, input channels K_IN, output channels K_OUT, depthwise conv (with FS=3) DW. See the Makefile for a full list. All simulation commands should be augmented with modified parameters, e.g.,

make stimuli H_IN=7 W_IN=3 K_OUT=32 K_IN=32
make sw-all run H_IN=7 W_IN=3 K_OUT=32 K_IN=32 gui=0

Contributors

Performance regressions

See https://pulp-platform.github.io/neureka/dev/bench/

License

This repository makes use of two licenses:

  • for all software: Apache License Version 2.0
  • for all hardware: Solderpad Hardware License Version 0.51

For further information have a look at the license files: LICENSE.hw, LICENSE.sw

References

  • A. S. Prasad, L. Benini and F. Conti, "Specialization meets Flexibility: a Heterogeneous Architecture for High-Efficiency, High-flexibility AR/VR Processing," 2023 60th ACM/IEEE Design Automation Conference (DAC), San Francisco, CA, USA, 2023, pp. 1-6, doi: 10.1109/DAC56929.2023.10247945.
  • A. S. Prasad, M. Scherer, F. Conti, D. Rossi, A. Di Mauro, M. Eggimann, J. T. Gómez, Z. Li, S. Shakib Sarwar, Z. Wang, B. De Salvo, and L. Benini, "Siracusa: A 16 nm Heterogenous RISC-V SoC for Extended Reality with At-MRAM Neural Engine," IEEE Journal of Solid-State Circuits, 2024 (accepted), arXiv: https://arxiv.org/abs/2312.14750.