Run Nengo models on the Loihi chip
A backend for running Nengo models on Intel’s Loihi architecture.
To use the backend, simply replace:
For more details and installation instructions, please see the documentation.
0.8.0 (June 23, 2019)
- Nengo Loihi now requires NxSDK version 0.8.5. (#225)
0.7.0 (June 21, 2019)
- Added RoundRobin allocator, which allows networks to be run across multiple chips (multi-chip) by assigning each ensemble to a different chip in a round-robin format. This allocator can be selected using the hardware_options argument when creating nengo_loihi.Simulator. (#197)
- Added support for Ensemble.neurons -> Ensemble connections. (#156)
- Switched to nengo-bones templating system for TravisCI config/scripts. (#204)
- It is no longer possible to pass network=None to Simulator. Previously this was possible, but unlikely to work as expected. (#202)
- Better error messages are raised when attempting to simulate networks in which certain objects participating in a learning rule are on-chip. (#202, #208, #209)
- Nengo Loihi now requires at least NxSDK version 0.8.0. (#218)
- The default intercept range set by nengo_loihi.set_defaults() is now (-1, 0.5), instead of (-0.5, 0.5). (#126)
- Obfuscated non-public information related to Intel’s NxSDK. (#228)
- The splitting and passthrough removal procedures were significantly refactored, which fixed an issue in which networks could be modified in the splitting process. (#202, #211)
- It is now possible to make connections and probes with object slices (e.g., nengo.Probe(my_ensemble)). (#202, #205, #206)
- We no longer disable the Nengo decoder cache for all models. (#202, #207)
- Transforms to on-chip neurons are now applied on-chip, which avoids scaling issues and large off-chip transforms. (#126)
0.6.0 (February 22, 2019)
- New Nengo transforms are supported, including nengo.Convolution. Many of the classes previously in conv.py have been moved to Nengo as part of this transition. The MNIST convnet example demonstrates the new syntax. (#142)
- Emulator now fails for any cx_base < 0, except -1 which indicates an unused axon. (#185)
- Noise now works correctly with small exponents on both the chip and emulator. Previously, the emulator did not allow very small exponents, and such exponents produced noise with the wrong magnitude on the chip. (#185)
- Models trained using NengoDL use tuning curves more similar to those of neuron on the chip, improving the accuracy of these model. (#140)
- Removed the NIF and NIFRate neuron types. These types were only used for encoding node values in spikes to send to the chip, which can be done just as well with nengo.SpikingRectifiedLinear neurons. (#185)
- Removed the unused/untested Synapse.set_diagonal_weights. (#185)
0.5.0 (February 12, 2019)
- Allow LIF.min_voltage to have effect. The exact minimum voltage on the chip is highly affected by discritization (since the chip only allows minimum voltages in powers of two), but this will at least provide something in the ballpark. (#169)
- Population spikes can now be used to send information more efficiently to the chip. Population spikes are necessary for larger models like those using CIFAR-10 data. (#161)
- PES learning in Nengo Loihi more closely matches learning in core Nengo. (#139)
- Learning in the emulator more closely matches learning on hardware. (#139)
- The neurons used to transmit decoded values on-chip can be configured. By default, we use ten pairs of heterogeneous neurons per dimension. (#132)
- Internal classes and functions have been reorganized and refactored. See the pull request for more details. (#159)
- Simulator now gives a warning if the user requests a progress bar, instead of an error. This avoids potential problems in nengo_gui and elsewhere. (#187)
- Nengo Loihi now supports NxSDK version 0.8.0. Versions 0.7.0 and 0.7.5 are still supported. (#188)
- We integrate current (U) and voltage (V) more accurately now by accounting for rounding during the decay process. This integral is used when discretizing weights and firing thresholds. This change significantly improves accuracy for many networks, but in particular dynamical systems like integrators. (#124, #114)
- Ensure things in the build and execution happen in a consistent order from one build/run to the next (by using OrderedDict, which is deterministic, instead of dict, which is not). This makes debugging easier and seeding consistent. (#151)
- Probes that use snips on the chip (when running with precompute=False) now deal with negative values correctly. (#169, #141)
- Filtering for probes on the chip is guaranteed to use floating-point now (so that the filtered output is correct, even if the underlying values are integers). (#169, #141)
- Neuron (spike) probes can now be filtered with synapse objects. (#182, #183)
0.4.0 (December 6, 2018)
- Added version tracking to documentation.
- An error is now raised if a learning rule is applied to a non-decoded connection. (#103)
- Switched documentation to new nengo-sphinx-theme. (#143)
0.3.0 (September 28, 2018)
- Models can now use the nengo.SpikingRectifiedLinear neuron model on both the emulator and hardware backends.
- Models can now run with different dt values (the default is 0.001, or 1 millisecond).
- Added support for Distributions on Connection transforms.
- Now compatible with NxSDK 0.7. We are currently not supporting older versions of NxSDK, but may in the future.
- Models will not be precomputed by default. To precompute models, you must explicitly pass precompute=True to nengo_loihi.Simulator.
- Models that do not run any objects on Loihi will raise an error.
- Ensemble intercept values are capped to 0.95 to fix issues with the current discretization method.
- Tuning curves now take into account the Loihi discretization, improving accuracy on most models.
- PES learning can now be done with multidimensional error signals.
- Manually reset spike probes when Simulator is initialized.
- Several fixes to filtering and connecting between objects on and off chip.
0.2.0 (August 27, 2018)
0.1.0 (July 4, 2018)
Pre-alpha release of Nengo Loihi for testing at the 2018 Telluride neuromorphic engineering conference. Thanks to all participants who tried out this early version of Nengo Loihi and provided feedback.
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