Deep Learning

Introduction

Lava-DL (lava-dl) is a library of deep learning tools within Lava that support offline training, online training and inference methods for various Deep Event-Based Networks.

There are two main strategies for training Deep Event-Based Networks: direct training and ANN to SNN converison.

Directly training the network utilizes the information of precise timing of events. Direct training is very accurate and results in efficient networks. However, directly training networks take a lot of time and resources.

On the other hand, ANN to SNN conversion is especially suitable for rate coded SNNs where we can leverage the fast training of ANN. These converted SNNs, however, require increased latency compared to directly trained SNNs.

Lava-DL provides an improved version of SLAYER for direct training of deep event based networks and a new ANN-SNN accelerated training approach called Bootstrap to mitigate high latency issue of conventional ANN-SNN methods for training Deep Event-Based Networks.

The lava-dl training libraries are independent of the core lava library since Lava Processes cannot be trained directly at this point. Instead, lava-dl is first used to train the model which can then be converted to a network of Lava processes using the netx library using platform independent hdf5 network description.

The library presently consists of

lava.lib.dl.slayer for natively training Deep Event-Based Networks.
lava.lib.dl.bootstrap for training rate coded SNNs.
lava.lib.dl.netx for training and deployment of event-based deep neural networks on traditional as well as neuromorphic backends.

Lava-dl also has the following external, fully compatible, plugin.

lava.lib.dl.decolle for training Deep SNNs with local learning and surrogate gradients. This extension is an implementation of DECOLLE learning repo to be fully compatible to lava-dl training tools. Refer here for the detailed description of the extension, examples and tutorials.:

J . Kaiser, H. Mostafa, and E. Neftci, Synaptic Plasticity Dynamics for Deep Continuous Local Learning (DECOLLE). pp 424, Frontiers in Neuroscience 2020.

More tools will be added in the future.

Lava-DL Workflow

Drawing

Typical Lava-DL workflow consists of:

Training: using lava.lib.dl.{slayer/bootstrap} which results in a hdf5 network description. Training usually consists of iterative cycle of architecture design, hyperparameter tuning, and backpropagation training.
Inference: using lava.lib.dl.netx which generates lava proces from the hdf5 network description of the trained network and enables inference on different backends.

Getting Started

End to end training tutorials

Deep dive tutorials

Dynamics and Neurons

Inference tutorials

SLAYER 2.0

SLAYER 2.0 (lava.lib.dl.slayer) is an enhanced version of SLAYER. Most noteworthy enhancements are: support for recurrent network structures, a wider variety of neuron models and synaptic connections (a complete list of features is here). This version of SLAYER is built on top of the PyTorch deep learning framework, similar to its predecessor. For smooth integration with Lava, lava.lib.dl.slayer supports exporting trained models using the platform independent hdf5 network exchange format.

In future versions, SLAYER will get completely integrated into Lava to train Lava Processes directly. This will eliminate the need for explicitly exporting and importing the trained networks.

Example Code

Import modules

import lava.lib.dl.slayer as slayer

Network Description

# like any standard pyTorch network
class Network(torch.nn.Module):
    def __init__(self):
        ...
        self.blocks = torch.nn.ModuleList([# sequential network blocks
                slayer.block.sigma_delta.Input(sdnn_params),
                slayer.block.sigma_delta.Conv(sdnn_params,  3, 24, 3),
                slayer.block.sigma_delta.Conv(sdnn_params, 24, 36, 3),
                slayer.block.rf_iz.Conv(rf_params, 36, 64, 3, delay=True),
                slayer.block.rf_iz.Conv(sdnn_cnn_params, 64, 64, 3, delay=True),
                slayer.block.rf_iz.Flatten(),
                slayer.block.alif.Dense(alif_params, 64*40, 100, delay=True),
                slayer.block.cuba.Recurrent(cuba_params, 100, 50),
                slayer.block.cuba.KWTA(cuba_params, 50, 50, num_winners=5)
            ])

    def forward(self, x):
        for block in self.blocks:
            # forward computation is as simple as calling the blocks in a loop
            x = block(x)
        return x

    def export_hdf5(self, filename):
        # network export to hdf5 format
        h = h5py.File(filename, 'w')
        layer = h.create_group('layer')
        for i, b in enumerate(self.blocks):
            b.export_hdf5(layer.create_group(f'{i}'))

Training

net = Network()
assistant = slayer.utils.Assistant(net, error, optimizer, stats)
...
for epoch in range(epochs):
    for i, (input, ground_truth) in enumerate(train_loader):
        output = assistant.train(input, ground_truth)
        ...
    for i, (input, ground_truth) in enumerate(test_loader):
        output = assistant.test(input, ground_truth)
        ...

Export the network

net.export_hdf5('network.net')

Bootstrap

In general ANN-SNN conversion methods for rate based SNN result in high latency of the network during inference. This is because the rate interpretation of a spiking neuron using ReLU acitvation unit breaks down for short inference times. As a result, the network requires many time steps per sample to achieve adequate inference results.

Bootstrap (lava.lib.dl.bootstrap) enables rapid training of rate based SNNs by translating them to an equivalent dynamic ANN representation which leads to SNN performance close to the equivalent ANN and low latency inference. More details here. It also supports hybrid training a mixed ANN-SNN network to minimize the ANN to SNN performance gap. This method is independent of the SNN model being used.

It has similar API as lava.lib.dl.slayer and supports exporting trained models using the platform independent hdf5 network exchange format.

Example Code

Import modules

import lava.lib.dl.bootstrap as bootstrap

Network Description

# like any standard pyTorch network
class Network(torch.nn.Module):
    def __init__(self):
        ...
        self.blocks = torch.nn.ModuleList([# sequential network blocks
                bootstrap.block.cuba.Input(sdnn_params),
                bootstrap.block.cuba.Conv(sdnn_params,  3, 24, 3),
                bootstrap.block.cuba.Conv(sdnn_params, 24, 36, 3),
                bootstrap.block.cuba.Conv(rf_params, 36, 64, 3),
                bootstrap.block.cuba.Conv(sdnn_cnn_params, 64, 64, 3),
                bootstrap.block.cuba.Flatten(),
                bootstrap.block.cuba.Dense(alif_params, 64*40, 100),
                bootstrap.block.cuba.Dense(cuba_params, 100, 10),
            ])

    def forward(self, x, mode):
        ...
        for block, m in zip(self.blocks, mode):
            x = block(x, mode=m)

        return x

    def export_hdf5(self, filename):
        # network export to hdf5 format
        h = h5py.File(filename, 'w')
        layer = h.create_group('layer')
        for i, b in enumerate(self.blocks):
            b.export_hdf5(layer.create_group(f'{i}'))

Training

net = Network()
scheduler = bootstrap.routine.Scheduler()
...
for epoch in range(epochs):
    for i, (input, ground_truth) in enumerate(train_loader):
        mode = scheduler.mode(epoch, i, net.training)
        output = net.forward(input, mode)
        ...
        loss.backward()
    for i, (input, ground_truth) in enumerate(test_loader):
        mode = scheduler.mode(epoch, i, net.training)
        output = net.forward(input, mode)
        ...

Export the network

net.export_hdf5('network.net')

Network Exchange (NetX) Library

For inference using Lava, Network Exchange Library (lava.lib.dl.netx) provides an automated API for loading SLAYER-trained models as Lava Processes, which can be directly run on a desired backend. lava.lib.dl.netx imports models saved via SLAYER using the hdf5 network exchange format. The details of hdf5 network description specification can be found here.

Example Code

Import modules

from lava.lib.dl.netx import hdf5

Load the trained network

# Import the model as a Lava Process
net = hdf5.Network(net_config='network.net')

Attach Processes for Input Injection and Output Readout

from lava.proc.io import InputLoader, BiasWriter, OutputReader

# Instantiate the processes
input_loader = InputLoader(dataset=testing_set)
bias_writer = BiasWriter(shape=input_shape)
output = OutputReader()

# Connect the input to the network:
input_loader.data_out.connect(bias_writer.bias_in)
bias_writer.bias_out.connect(net.in_layer.bias)

# Connect network-output to the output process
net.out_layer.neuron.s_out.connect(output.net_output_in)

from lava.proc import io

# Instantiate the processes
dataloader = io.dataloader.SpikeDataloader(dataset=test_set)
output_logger = io.sink.RingBuffer(shape=net.out_layer.shape, buffer=num_steps)
gt_logger = io.sink.RingBuffer(shape=(1,), buffer=num_steps)

# Connect the input to the network:
dataloader.ground_truth.connect(gt_logger.a_in)
dataloader.s_out.connect(net.in_layer.neuron.a_in)

# Connect network-output to the output process
net.out_layer.out.connect(output_logger.a_in)

Run the network

from lava.magma import run_configs as rcfg
from lava.magma import run_conditions as rcnd

net.run(condition=rcnd.RunSteps(total_run_time), run_cfg=rcfg.Loihi1SimCfg())

Detailed Description

Detailed description: