nngen VS larq

Compare nngen vs larq and see what are their differences.

nngen

NNgen: A Fully-Customizable Hardware Synthesis Compiler for Deep Neural Network (by NNgen)
neural-network high-level-synthesis Hardware veriloggen pyverilog Deep Learning Compiler verilog-hdl Python Onnx
Source Code
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larq

An Open-Source Library for Training Binarized Neural Networks (by larq)
Deep Learning Machine Learning Tensorflow Python binarized-neural-networks quantized-neural-networks Keras Binder larq
Source Code
larq.dev
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nngen larq
1 2
318 693
3.1% 1.0%
4.9 7.5
7 months ago 4 days ago
Python Python
Apache License 2.0 Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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nngen

Posts with mentions or reviews of nngen. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-03-09.

larq

Posts with mentions or reviews of larq. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-03-09.
  • Running CNN on ATmega328P
    1 project | /r/embedded | 1 Apr 2022
    You quantize the model parameters i.e., don't just send the model in which uses floating point math instead change it to fixed point. This has 2 advantages 1) a pure size reduction and 2) most low power MCU's don't have float point multipliers but do have single cycle fixed point multipliers. This is a classic DSP trick used for a long time. The real research aspects come-in as you start dropping below 8-bit; even coming down to single-bit in some cases(see Larq)
  • Simplifying AI to FPGA deployment, looking for opportunities
    3 projects | /r/FPGA | 9 Mar 2022
    It is a difficult question. I work almost exclusively with open source, so I'm not much use to give you advice. Maybe you can see how Plumerai handles things -- they have some stuff proprietary, but they've also open-sourced their BNN Larq stuff: https://github.com/larq/larq

What are some alternatives?

When comparing nngen and larq you can also consider the following projects:

TensorLayer - Deep Learning and Reinforcement Learning Library for Scientists and Engineers

finn-examples - Dataflow QNN inference accelerator examples on FPGAs

Pyverilog - Python-based Hardware Design Processing Toolkit for Verilog HDL

model-optimization - A toolkit to optimize ML models for deployment for Keras and TensorFlow, including quantization and pruning.

data-science-ipython-notebooks - Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

PipelineC - A C-like hardware description language (HDL) adding high level synthesis(HLS)-like automatic pipelining as a language construct/compiler feature.

nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

dace - DaCe - Data Centric Parallel Programming

nngen vs TensorLayer larq vs finn-examples nngen vs Pyverilog larq vs model-optimization nngen vs finn-examples larq vs data-science-ipython-notebooks nngen vs PipelineC nngen vs nni nngen vs dace