All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning:

  • Added for new features.

  • Changed for changes in existing functionality.

  • Deprecated for soon-to-be removed features.

  • Removed for now removed features.

  • Fixed for any bug fixes.

  • Security in case of vulnerabilities.




  • Fixed compilation error for CUDA 12.1. (#500)



0.7.1 - 2023/03/24


  • Updated the CLI Cloud runner code to support inference experiment result. (#491)

  • Read weights is done with least-square estimation method. (#489)


  • Realistic read / write behavior was broken for some tiles. (#489)


  • Torch minimal version has changed to version 1.9. (#489)

  • Realistic read / write is now achieved by read_weights and program_weights. (#489)


  • The tile methods get/set_weights_realistic are removed. (#489)

0.7.0 - 2023/01/04


  • Reset tiles method (#456)

  • Added many new analog MAC non-linearties (forward / backward pass). (#456)

  • Polynomial weight noise for hardware-aware training. (#456)

  • Remap functionality for hardware-aware training. (#456)

  • Input range estimation for InferenceRPUConfig. (#456)

  • CUDA always syncs and added non-blocking option if not wished. (#456)

  • Fitting utility for fitting any device model to conductance measurements. (#456)

  • Added PowStepReferenceDevice for easy subtraction of symmetry point. (#456)

  • Added SoftBoundsReferenceDevice for easy subtraction of symmetry point. (#456)

  • Added stand-alone functions for applying inference drift to any model. (#419)

  • Added Example 24: analog inference and hardware-aware training on BERT with the SQUAD task. (#440)

  • Added Example 23: how to use AnalogTile directly to implement an analog matrix-vector product without using pytorch modules. (#393)

  • Added Example 22: 2 layer LSTM network trained on War and Peace dataset. (#391)

  • Added a new notebook for exploring analog sensitivities. (#380)

  • Remapping functionality for InferenceRPUConfig. (#388)

  • Inference cloud experiment and runners. (#410)

  • Added analog_modules generator in AnalogSequential. (#410)

  • Added SKIP_CUDA_TESTS to manually switch off the CUDA tests.

  • Enabling comparisons of RPUConfig instances. (#410)

  • Specific user-defined function for layer-wise setting for RPUConfigs in conversions. (#412)

  • Added stochastic rounding options for MixedPrecisionCompound. (#418)

  • New remap parameter field and functionality in InferenceRPUConfig (#423).

  • Tile-level weight getter and setter have apply_weight_scaling argument. (#423)

  • Pre and post-update / backward / forward methods in BaseTile for easier user-defined modification of pre and/or post-processings of a tile. (#423)

  • Type-checking for RPUConfig fields. (#424)


  • Decay fix for compound devices. (#463)

  • RPUCuda backend update with many fixes. (#456)

  • Missing zero-grad call in example 02. (#446)

  • Indexing error in OneSidedDevice for CPU. (#447)

  • Analog summary error when model is on cuda device. (#392)

  • Index error when loading the state dict with a model use previously. (#387)

  • Weights that were not contiguous could have been set wrongly. (#388)

  • Programming noise would not be applied if drift compensation was not used. (#389)

  • Loading a new model state dict for inference does not overwrite the noise model setting. (#410)

  • Avoid AnalogContext copying of self pointers. (#410)

  • Fix issue that drift compensation is not applied to conv-layers. (#412)

  • Fix issue that noise modifiers are not applied to conv-layers. (#412)

  • The CPU AnalogConv2d layer now uses unfolded convolutions instead of indexed covolutions (that are efficient only for GPUs). (#415)

  • Fix issue that write noise hidden weights are not transferred to pytorch when using get_hidden_parameters in case of CUDA. (#417)

  • Learning rate scaling due to output scales. (#423)

  • WeightModifiers of the InferenceRPUConfig are no longer called in the forward pass, but instead in the post_update_step method to avoid issues with repeated forward calls. (#423)

  • Fix training learn_out_scales issue after checkpoint load. (#434)


  • Pylint / mypy / pycodestyle / protobuf version bump (#456)

  • All configs related classes can now be imported from aihwkit.simulator.config. (#456)

  • Weight noise visualization now shows the programming noise and drift noise differences. (#389)

  • Concatenate the gradients before applying to the tile update function (some speedup for CUDA expected). (#390)

  • Drift compensation uses eye instead of ones for readout. (#412)

  • weight_scaling_omega_columnwise parameter in MappingParameter is now called weight_scaling_columnwise. (#423)

  • Tile-level weight getter and setter now use Tensors instead of numpy arrays. (#423)

  • Output scaling and mapping scales are now distiniguished, only the former is learnable. (#423)

  • Renamed learn_out_scaling_alpha parameter in MappingParameter to learn_out_scaling and columnwise learning has a separate switch out_scaling_columnwise. (#423)


  • Input weight_scaling_omega argument in analog layers is deprecated. (#423)


  • The _scaled versions of the weight getter and setter methods are removed. (#423)

0.6.0 - 2022/05/16


  • Set weights can be used to re-apply the weight scaling omega. (#360)

  • Out scaling factors can be learnt even if weight scaling omega was set to 0. (#360)

  • Reverse up / down option for LinearStepDevice. (#361)

  • Generic Analog RNN classes (LSTM, RNN, GRU) uni or bidirectional. (#358)

  • Added new PiecewiseStepDevice where the update-step response function can be arbitrarily defined by the user in a piece-wise linear manner. It can be conveniently used to fit any experimental device data. (#356)

  • Several enhancements to the public documentations: added a new section for hw-aware training, refreshed the reference API doc, and added the newly supported LSTM layers and the mapped conv layers. (#374)


  • Legacy checkpoint load with alpha scaling. (#360)

  • Re-application of weight scaling omega when loading checkpoints. (#360)

  • Write noise was not correctly applied for CUDA if dw_min_std=0. (#356)


  • The set_alpha_scale and get_alpha_scale methods of the C++ tiles are removed. (#360)

  • The lowest supported Python version is now 3.7, as 3.6 has reached end-of-life. Additionally, the library now officially supports Python 3.10. (#368)

0.5.1 - 2022/01/27


  • Load model state dict into a new model with modified RPUConfig. (#276)

  • Visualization for noise models for analog inference hardware simulation. (#278)

  • State independent inference noise model. (# 284)

  • Transfer LR parameter for MixedPrecisionCompound. (#283)

  • The bias term can now be handled either by the analog or digital domain by controlling the digital_bias layer parameter. (#307)

  • PCM short-term weight noise. (#312)

  • IR-drop simulation across columns during analog mat-vec. (#312)

  • Transposed-read for TransferCompound. (#312)

  • BufferedTranferCompound and TTv2 presets. (#318)

  • Stochastic rounding for MixedPrecisionCompound. (#318)

  • Decay with arbitrary decay point (to reset bias). (#319)

  • Linear layer AnalogLinearMapped which maps a large weight matrix onto multiple analog tiles. (#320)

  • Convolution layers AnalogConvNdMapped which maps large weight matrix onto multiple tiles if necessary. (#331)

  • In the new mapping field of RPUConfig the max tile input and output sizes can be configured for the *Mapped layers. (#331)

  • Notebooks directory with several notebook examples (#333, #334)

  • Analog information summary function. (#316)

  • The alpha weight scaling factor can now be defined as learnable parameter by switching learn_out_scaling_alpha in the rpu_config.mapping parameters. (#353)


  • Removed GPU warning during destruction when using multiple GPUs. (#277)

  • Fixed issue in transfer counter for mixed precision in case of GPU. (#283)

  • Map location keyword for load / save observed. (#293)

  • Fixed issue with CUDA buffer allocation when batch size changed. (#294)

  • Fixed missing load statedict for AnalogSequential. (#295)

  • Fixed issue with hierarchical hidden parameter settings. (#313)

  • Fixed serious issue that loaded model would not update analog gradients. (#320)

  • Fixed cuda import in examples. (#320)


  • The inference noise models are now located in aihwkit.inference. (#281)

  • Analog state dict structure `has changed (shared weight are not saved). (#293)

  • Some of the parameter names of theTransferCompound have changed. (#312)

  • New fast learning rate parameter for TransferCompound, SGD learning rate then is applied on the slow matrix (#312).

  • The fixed_value of WeightClipParameter is now applied for all clipping types if set larger than zero. (#318)

  • The use of generators for analog tiles of an AnalogModuleBase. (#320)

  • Digital bias is now accessable through MappingParameter. (#331)

  • The aihwkit documentation. New content around analog ai concepts, training presets, analog ai optimizers, new references, and examples. (#348)

  • The weight_scaling_omega can now be defined in the rpu_config.mapping. (#353)


  • The module aihwkit.simulator.noise_models has been depreciated in favor of aihwkit.inference. (#281)

0.4.0 - 2021/06/25


  • A number of new config presets added to the library, namely EcRamMOPreset, EcRamMO2Preset, EcRamMO4Preset, TikiTakaEcRamMOPreset, MixedPrecisionEcRamMOPreset. These can be used for tile configuration (rpu_config). They specify a particular device and optimizer choice. (#207)

  • Weight refresh mechanism for OneSidedUnitCell to counteract saturation, by differential read, reset, and re-write. (#209)

  • Complex cycle-to-cycle noise for ExpStepDevice. (#226)

  • Added the following presets: PCMPresetDevice (uni-directional), PCMPresetUnitCell (a pair of uni-directional devices with periodical refresh) and a MixedPrecisionPCMPreset for using the mixed precision optimizer with a PCM pair. (#226)

  • AnalogLinear layer now accepts multi-dimensional inputs in the same way as PyTorch’s Linear layer does. (#227)

  • A new AnalogLSTM module: a recurrent neural network that uses AnalogLinear. (#240)

  • Return of weight gradients for InferenceTile (only), so that the gradient can be handled with any PyTorch optimizer. (#241)

  • Added a generic analog optimizer AnalogOptimizer that allows extending any existing optimizer with analog-specific features. (#242)

  • Conversion tools for converting torch models into a model having analog layers. (#265)


  • Renamed the DifferenceUnitCell to OneSidedUnitCell which more properly reflects its function. (#209)

  • The BaseTile subclass that is instantiated in the analog layers is now retrieved from the new RPUConfig.tile_class attribute, facilitating the use of custom tiles. (#218)

  • The default parameter for the dataset constructor used by BasicTraining is now the train=bool argument. If using a dataset that requires other arguments or transforms, they can now be specified via overriding get_dataset_arguments() and get_dataset_transform(). (#225)

  • AnalogContext is introduced, along with tile registration function to handle arbitrary optimizers, so that re-grouping param groups becomes unnecessary. (#241)

  • The AnalogSGD optimizer is now implemented based on the generic analog optimizer, and its base module is aihwkit.optim.analog_optimizer. (#242)

  • The default refresh rate is changed to once per mini-batch for PCMPreset (as opposed to once per mat-vec). (#243)


  • Deprecated the CudaAnalogTile and CudaInferenceTile and CudaFloatingPointTile. Now the AnalogTile can be either on cuda or on cpu (determined by the tile and the device attribute) similar to a torch Tensor. In particular, call of cuda() does not change the AnalogTile to CudaAnalogTile anymore, but only changes the instance in the tile field, which makes in-place calls to cuda() possible. (#257)


  • Removed weight and bias of analog layers from the module parameters as these parameters are handled internally for analog tiles. (#241)


  • Fixed autograd functionality for recurrent neural networks. (#240)

  • N-D support for AnalogLinear. (#227)

  • Fixed an issue in the Experiments that was causing the epoch training loss to be higher than the epoch validation loss. (#238)

  • Fixed “Wrong device ordinal” errors for CUDA which resulted from a known issue of using CUB together with pytorch. (#250)

  • Renamed persistent weight hidden parameter field to persistent_weights. (#251)

  • Analog tiles now always move correctly to CUDA when model.cuda() or is used. (#252, #257)

  • Added an error message when wrong tile class is used for loading an analog state dict. (#262)

  • Fixed MixedPrecisionCompound being bypassed with floating point compute. (#263)

0.3.0 - 2021/04/14


  • New analog devices:

    • A new abstract device (MixedPrecisionCompound) implementing an SGD optimizer that computes the rank update in digital (assuming digital high precision storage) and then transfers the matrix sequentially to the analog device, instead of using the default fully parallel pulsed update. (#159)

    • A new device model class PowStepDevice that implements a power-exponent type of non-linearity based on the Fusi & Abott synapse model. (#192)

    • New parameterization of the SoftBoundsDevice, called SoftBoundsPmaxDevice. (#191)

  • Analog devices and tiles improvements:

    • Option to choose deterministic pulse trains for the rank-1 update of analog devices during training. (#99)

    • More noise types for hardware-aware training for inference (polynomial). (#99)

    • Additional bound management schemes (worst case, average max, shift). (#99)

    • Cycle-to-cycle output referred analog multiply-and-accumulate weight noise that resembles the conductance dependent PCM read noise statistics. (#99)

    • C++ backend improvements (slice backward/forward/update, direct update). (#99)

    • Option to excluded bias row for hardware-aware training noise. (#99)

    • Option to automatically scale the digital weights into the full range of the simulated crossbar by applying a fixed output global factor in digital. (#129)

    • Optional power-law drift during analog training. (#158)

    • Cleaner setting of dw_min using device granularity. (#200)

  • PyTorch interface improvements:

    • Two new convolution layers have been added: AnalogConv1d and AnalogConv3d, mimicking their digital counterparts. (#102, #103)

    • The .to() method can now be used in AnalogSequential, along with .cpu() methods in analog layers (albeit GPU to CPU is still not possible). (#142, #149)

  • New modules added:

    • A library of device presets that are calibrated to real hardware data, namely ReRamESPresetDevice, ReRamSBPresetDevice, ECRamPresetDevice, CapacitorPresetDevice, and device presets that are based on models in the literature, e.g. GokmenVlasovPresetDevice and IdealizedPresetDevice. They can be used defining the device field in the RPUConfig. (#144)

    • A library of config presets, such as ReRamESPreset, Capacitor2Preset, TikiTakaReRamESPreset, and many more. These can be used for tile configuration (rpu_config). They specify a particular device and optimizer choice. (#144)

    • Utilities for visualization the pulse response properties of a given device configuration. (#146)

    • A new aihwkit.experiments module has been added that allows creating and running specific high-level use cases (for example, neural network training) conveniently. (#171, #172)

    • A CloudRunner class has been added that allows executing experiments in the cloud. (#184)


  • The minimal PyTorch version has been bumped to 1.7+. Please recompile your library and update the dependencies accordingly. (#176)

  • Default value for TransferCompound for transfer_every=0 (#174).


  • Issue of number of loop estimations for realistic reads. (#192)

  • Fixed small issues that resulted in warnings for windows compilation. (#99)

  • Faulty backward noise management error message removed for perfect backward and CUDA. (#99)

  • Fixed segfault when using diffusion or reset with vector unit cells for CUDA. (#129)

  • Fixed random states mismatch in IoManager that could cause crashed in same network size and batch size cases for CUDA, in particular for TransferCompound. (#132)

  • Fixed wrong update for TransferCompound in case of transfer_every smaller than the batch size. (#132, #174)

  • Period in the modulus of TransferCompound could become zero which caused a floating point exception. (#174)

  • Ceil instead of round for very small transfers in TransferCompound (to avoid zero transfer for extreme settings). (#174)


  • The legacy NumpyAnalogTile and NumpyFloatingPointTile tiles have been finally removed. The regular, tensor-powered aihwkit.simulator.tiles tiles contain all their functionality and numerous additions. (#122)

0.2.1 - 2020/11/26

  • The rpu_config is now pretty-printed in a readable manner (excluding the default settings and other readability tweak). (#60)

  • Added a new ReferenceUnitCell which has two devices, where one is fixed and the other updated and the effective weight is computed a difference between the two. (#61)

  • VectorUnitCell accepts now arbitrary weighting schemes that can be user-defined by using a new gamma_vec property that specifies how to combine the unit cell devices to form the effective weight. (#61)


  • The unit cell items in aihwkit.simulator.configs have been renamed, removing their Device suffix, for having a more consistent naming scheme. (#57)

  • The Exceptions raised by the library have been revised, making use in some cases of the ones introduced in a new aihwkit.exceptions module. (#49)

  • Some VectorUnitCell properties have been renamed and extended with an update policy specifying how to select the hidden devices. (#61)

  • The pybind11 version required has been bumped to 2.6.0, which can be installed from pip and makes system-wide installation no longer required. Please update your pybind11 accordingly for compiling the library. (#44)


  • The BackwardIOParameters specialization has been removed, as bound management is now automatically ignored for the backward pass. Please use the more general IOParameters instead. (#45)


  • Serialization of Modules that contain children analog layers is now possible, both when using containers such as Sequential and when using analog layers as custom Module attributes. (#74, #80)

  • The build system has been improved, with experimental Windows support and supporting using CUDA 11 correctly. (#58, #67, #68)

0.2.0 - 2020/10/20


  • Added more types of resistive devices: IdealResistiveDevice, LinearStep, SoftBounds, ExpStep, VectorUnitCell, TransferCompoundDevice, DifferenceUnitCell. (#14)

  • Added a new InferenceTile that supports basic hardware-aware training and inference using a statistical noise model that was fitted by real PCM devices. (#25)

  • Added a new AnalogSequential layer that can be used in place of Sequential for easier operation on children analog layers. (#34)


  • Specifying the tile configuration (resistive device and the rest of the properties) is now based on a new RPUConfig family of classes, that is passed as a rpu_config argument instead of resistive_device to Tiles and Layers. Please check the aihwkit.simulator.config module for more details. (#23)

  • The different analog tiles are now organized into a aihwkit.simulator.tiles package. The internal IndexedTiles have been removed, and the rest of previous top-level imports have been kept. (#29)


  • Improved package compatibility when using non-UTF8 encodings (version file, package description). (#13)

  • The build system can now detect and use openblas directly when using the conda-installable version. (#22)

  • When using analog layers as children of another module, the tiles are now correctly moved to CUDA if using AnalogSequential (or by the optimizer if using regular torch container modules). (#34)

0.1.0 - 2020/09/17


  • Initial public release.

  • Added rpucuda C++ simulator, exposed through a pybind interface.

  • Added a PyTorch AnalogLinear neural network model.

  • Added a PyTorch AnalogConv2d neural network model.