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Please refer to getting started guides within Startup Materials /wiki/spaces/NAP/pages/4522014[also available in nxsdk-apps-0.8.5.tar.gz/docs] for installation instructions as additional steps have been added to the setup. There are few API changes in this release which have been enlisted in the API Changes/Deprecation section below.
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Composable networks
Connection sharing (i.e. synapse sharing)
Following modules have been added under nxsdk_modules (Each module comes with tutorials to demonstrate applications or re-usable feature set)
noise filter : Performs noise filtering on a DVS stream.
dvs : Provides an interface to DVS sensors. Currently only supports the DAVIS240C.
trace injection : A module showing how non-local information can be injected into traces available to the learning rules.
path planning : Path planning model is based on the planning algorithm modeled after the operational principles of hippocampal place cells. The algorithm infers associations between neurons in a network from the asymmetric effects of STDP on a propagating sequence of spikes.
epl : A neuromorphic one-shot learning algorithm which can learn and recall patterns using a spatio-temporal attractor network inspired by the neural circuitry of the external plexiform layer (EPL) of the mammalian olfactory bulb.
Jupyter Tutorials | Feature Covered | Description | Category |
|---|---|---|---|
p_composable_networks.ipynb | Composable networks | This tutorial illustrates how to connect multiple NxNet objects | NxNet |
q_connection_sharing.ipynb | Connection sharing | This tutorial illustrates the use of shared connections | NxNet |
r_stubs_and_netmodules.ipynb | Stubs and NetModules | This tutorial illustrates the use of connection stubs and making a module | NxNet |
NxCore
Axon Compiler now supports remote population axons
BasicSpikeGenerator now supports pop16 and pop32 spike type
General
Several improvements in transfer performance (Network/IO) should speed up data transfer. Speedup has been observed in configuring chips/registers and probe post-processing.
Speed and memory use for creating large compartment groups and connection groups have both been significantly improved.
SpikeOutputPorts allow fast communication of spikes from Loihi to a pipe on the host when using Kapoho Bay
Improved DVS support for Kapoho Bay
Support for custom partitioners
Setup instructions have been updated for KapohoBay and boards which are maintained outside INRC. Please revisit getting started guides
Following modules have been added under nxsdk_modules :
Path Planning : Path planning module is based on the planning algorithm modeled after the operational principles of hippocampal place cells. The algorithm infers associations between neurons in a network from the asymmetric effects of STDP on a propagating sequence of spikes.
API Changes/Deprecations
NxNet.createConnection(src, dst, ...) and NxNet.createConnectionGroup(src, dst, ...) are no longer supported. The supported way to create connections is src.connect(dst, ...).
Spike Probes will only start accumulating spikes only after tstart has been configured and any previous spikes are discarded.
Synapse recompilation is not supported. Re-encoding should be done via SNIPs which provide a faster way for re-configuration.
The group id property (for example, CompartmentGroup.id) has been renamed to groupId (i.e. CompartmentGroup.groupId) to avoid confusion with python id() function.
nxDriver is deprecated as a property of Graph/N2Board. Instead please use board.executor.
startDriver is being deprecated. Instead please use board.start.
Spike Generators have been moved within nxsdk.graph.nxinputgen module. For e.g. to import BasicSpikeGenerator you would import nxsdk.graph.nxinputgen.nxinputgen.BasicSpikeGenerator.
Spike Receivers will now only send incremental data since the last invocation.
board.dump() and board.load() have been renamed to board.dumpNeuroCores() and board.loadNeuroCores() respectively.
The DVS noise filter interface has changed. See the corresponding tutorials for examples of how to use it.
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