Lava-DL NetX

Lava-DL NetX library lava.lib.dl.netx automates deep learning network model exchange to/from LAVA from/to other frameworks. At the moment, we support a simple platform independent hdf5 network description protocol. In furture we will extend network exchange support to other neural network exchange formats.

Loading a model to Lava is as simple as:

from lava.lib.dl.netx import hdf5
# Import the model as a Lava Process
net = hdf5.Network(net_config='')


The hdf5 network description protocol is described below:

HDF5 description protocol

  • The computational graph is represented layer-wise in the layer field of the hdf5 file.

  • The layer id is assigned serially from 0 to n-1 starting from input to output.

    • By default, a sequential connection is assumed.

    • Skip/recurrent connections are preceded by a concatenate layer that connects to pervious layer plus a list of non-sequential layers identified by their id.

    • Each layer entry consts a minimum of shape and type field. Other relevant fileds can be addes as necessary.

      • shape entry is a tuple/list in (x, y, z) format.

      • type entry is a string that describes the layer type. See below for a list of supported types.

      • neuron field describes the compartment model and it’s parameter.

        • default neuron type is CUBA-LIF.

        |->layer # description of network layer blocks such as input, dense, conv, pool, flatten, average
        |   |->0
        |   |  |->{shape, type, ...} # each layer description has at least shape and type attribute
        |   |->1
        |   |  |->{shape, type, ...}
        |   :
        |   |->n-1
        |      |->{shape, type, ...}
        | # other fields (not used for network exchange)
        |->simulation # simulation description
        |   |->Ts # sampling time. Usually 1
        |   |->tSample # length of the sample to run

Supported layer types

input  : {shape, type}
flatten: {shape, type}
average: {shape, type}
concat : {shape, type, layers}
dense  : {shape, type, neuron, inFeatures, outFeatures, weight, delay(if available)}
pool   : {shape, type, neuron, kernelSize, stride, padding, dilation, weight}
conv   : {shape, type, neuron, inChannels, outChannels, kernelSize, stride,
                        |      padding, dilation, groups, weight, delay(if available)}
                        |-> this is the description of the compartment parameters
                        |-> {iDecay, vDecay, vThMant, refDelay, ... (other additional params)}