chisel4ml 0.3.2

A Chisel based hardware generation library for highly quantized neural networks.

Chisel4ml

Chisel4ml is an open-source library for generating dataflow architectures inspired by the hls4ml library.

Instalation: from pip

1. pip install chisel4ml.
2. Download a matching jar from github relases.
3. To test first run java -jar chisel4ml.jar (You can change the port and temporary directory using -p and -d (use --help for info)
4. Paste the Python code bellow into a file and run python script.py

import numpy as np
import qkeras
import tensorflow as tf
from chisel4ml import optimize, generate

w1 = np.array([[1, 2, 3, 4], [-4, -3, -2, -1], [2, -1, 1, 1]])
b1 = np.array([1, 2, 0, 1])
w2 = np.array([-1, 4, -3, -1]).reshape(4, 1)
b2 = np.array([2])

x = x_in = tf.keras.layers.Input(shape=3)
x = qkeras.QActivation(
    qkeras.quantized_bits(bits=4, integer=3, keep_negative=True)
)(x)
x = qkeras.QDense(
    4,
    kernel_quantizer=qkeras.quantized_bits(
        bits=4, integer=3, keep_negative=True, alpha=np.array([0.5, 0.25, 1, 0.25])
    ),
)(x)
x = qkeras.QActivation(qkeras.quantized_relu(bits=3, integer=3))(x)
x = qkeras.QDense(
    1,
    kernel_quantizer=qkeras.quantized_bits(
        bits=4, integer=3, keep_negative=True, alpha=np.array([0.125])
    ),
)(x)
x = qkeras.QActivation(qkeras.quantized_relu(bits=3, integer=3))(x)
model = tf.keras.Model(inputs=[x_in], outputs=[x])
model.compile()
model.layers[2].set_weights([w1, b1])
model.layers[4].set_weights([w2, b2])
data = np.array(
    [
        [0.0, 0.0, 0.0],
        [0.0, 1.0, 2.0],
        [2.0, 1.0, 0.0],
        [4.0, 4.0, 4.0],
        [7.0, 7.0, 7.0],
        [6.0, 0.0, 7.0],
        [3.0, 3.0, 3.0],
        [7.0, 0.0, 0.0],
        [0.0, 7.0, 0.0],
        [0.0, 0.0, 7.0],
    ]
)


opt_model = optimize.qkeras_model(model)
circuit = generate.circuit(opt_model)
for x in data:
    sw_res = opt_model.predict(np.expand_dims(x, axis=0))
    hw_res = circuit(x)
    assert np.array_equal(sw_res.flatten(), hw_res.flatten())
circuit.delete_from_server()

This will generate a circuit of a simple two layer fully-connected neural network, and store it in /tmp/.chisel4ml/circuit0. If you have verilator installed you can also add the argument: use_verilator=True in the generate.circuit function. In the first case only a firrtl file be generated (this can be converted to verilog using firtool), if you use verilator, however, a SystemVerilog file will also be created.

Installation: from source

1. Install mill build tool.
2. Install python 3.8-3.10
3. Create environment python -m venv venv/
4. Activate environment (Linux)source venv/bin/activate
   • Windows .\venv\Scripts\activate
5. Upgrade pip python -m pip install --upgrade pip
6. Install chisel4ml pip install -ve .[dev]
7. Build Python protobuf code make
8. Build Scala code mill chisel4ml.assembly
9. Start a chisel4ml server java -jar ./out/chisel4ml/assembly.dest/out.jar
10. In another terminal run tests pytest -svv