Abstract: A method may include embedding, in a hidden layer and/or an output layer of a first machine learning model, a first digital watermark. The first digital watermark may correspond to input samples altering the low probabilistic regions of an activation map associated with the hidden layer of the first machine learning model. Alternatively, the first digital watermark may correspond to input samples rarely encountered by the first machine learning model. The first digital watermark may be embedded in the first machine learning model by at least training, based on training data including the input samples, the first machine learning model. A second machine learning model may be determined to be a duplicate of the first machine learning model based on a comparison of the first digital watermark embedded in the first machine learning model and a second digital watermark extracted from the second machine learning model.

Abstract: A system may include a processor and a memory. The memory may include program code that provides operations when executed by the processor. The operations may include: partitioning, based at least on a resource constraint of a platform, a global machine learning model into a plurality of local machine learning models; transforming training data to at least conform to the resource constraint of the platform; and training the global machine learning model by at least processing, at the platform, the transformed training data with a first of the plurality of local machine learning models.

Abstract: Embodiments of the present disclosure include systems and methods for providing hierarchical and shared exponent floating point data types. First and second shared exponent values are determined based on exponent values of a plurality of floating point values. A third shared exponent value is determined based the first shared exponent value and the second shared exponent value. First and second difference values are determined based on the first shared exponent value, the second shared exponent value, and the third shared exponent value. Sign values and mantissa values are determined for the plurality of floating point values. The sign value and the mantissa value for each floating point value in the plurality of floating point values, the third shared exponent value, the first difference value, and the second difference value are stored in a data structure for a shared exponent floating point data type.

Abstract: A method for sparse matrix multiplication comprises receiving a first block having M elements in a first dimension, and parsing the first block of M elements into a first set of B sub-blocks including MB elements in the first dimension. A first sparsity mask having S % sparsity is applied to the first block of elements, such that each of the first set of B sub-blocks has S % sparsity. A second block is received having M elements in a second dimension, and is parsed into a second set of B sub-blocks that include MB elements in the second dimension. A second sparsity mask having S?% sparsity is applied to the second block of elements, such that S?% of the second set of B sub-blocks have 100% sparsity and (100?S?)% of the second set of B sub-blocks have 0% sparsity. The first and second blocks are then matrix multiplied.

Abstract: Embodiments of the present disclosure include systems and methods for providing model customizations of transformers for improved efficiency. A first set of settings for a transformer model is received. Based on the first set of settings, a second set of settings for the transformer model is determined. The first set of settings and the second set of settings are used to configure and train the transformer model.

Abstract: Embodiments of the present disclosure include systems and methods for sparsifying vectors for neural network models based on overlapping windows. A window is used to select a first set of elements in a vector of elements. A first element is selected from the first set of elements having the highest absolute value. The window is slid along the vector by a defined number of elements. The window is used to select a second set of elements in the vector, wherein the first set of elements and the second set of elements share at least one common element. A second element is selected from the second set of elements having the highest absolute value.

Abstract: A computing system is configured to implement a deep neural network comprising an input layer for receiving inputs applied to the deep neural network, an output layer for outputting inferences based on the received inputs, and a plurality of hidden layers interposed between the input layer and the output layer. A plurality of nodes selectively operate on the inputs to generate and cause outputting of the inferences, wherein operation of the nodes is controlled based on parameters of the deep neural network. A sparsity controller is configured to selectively apply a plurality of different sparsity states to control parameter density of the deep neural network. A quantization controller is configured to selectively quantize the parameters of the deep neural network in a manner that is sparsity-dependent, such that quantization applied to each parameter is based on which of the plurality of different sparsity states applies to the parameter.

Abstract: A method for operating a machine learning model is presented. The machine learning model includes a plurality of sequential transformer blocks. The method comprises receiving input data at a transformer block and processing the input data via a mixture of experts layer. At an auxiliary classifier, a measure of perplexity of the processed input data is determined. Based on the determined measure of perplexity, one or more experts in a downstream transformer block that will subsequently process the input data are indicated. Weight matrices are then fetched for the indicated one or more experts.

Abstract: A method is presented for operating a machine learning model including one or more mixture of experts layers. The method comprises receiving one or more input data shards at a routing gate network for a mixture of experts layer comprising a plurality of neural network experts. One or more neural network experts in the mixture of experts layer is designated layer to evaluate each input data shard. For each designated neural network expert, a weight matrix is retrieved having a predetermined sparsity to generate a sparsified designated neural network expert. Each input data shard is evaluated with a respective sparsified designated neural network expert.

Abstract: A method is presented for training a neural network. For a weight matrix having integer dimensions M1 in a first dimension and an integer dimension M2 in a second dimension, a first balanced sparsity mask is generated that is an N1 of M1 mask in the first dimension. The first balanced sparsity mask is applied to the weight matrix during inference. A second balanced sparsity mask is generated for a transpose of the weight matrix. The second balanced sparsity mask is an N2 of M2 mask in the second dimension. The second balanced sparsity mask is applied to the transpose of the weight matrix during backpropagation.

Abstract: A system for training a neural network receives training data and performing lower precision format training calculations using lower precision format data at one or more training phases. One or more results from the lower precision format training calculations are converted to higher precision format data, and higher precision format training calculations are performed using the higher precision format data at one or more additional training phases. The neural network is modified using the results from the one or more additional training phases. The mixed precision format training calculations train the neural network more efficiently, while maintaining an overall accuracy.

Abstract: Technology related to training a neural network accelerator using mixed precision data formats is disclosed. In one example of the disclosed technology, a neural network accelerator is configured to accelerate a given layer of a multi-layer neural network. An input tensor for the given layer can be converted from a normal-precision floating-point format to a quantized-precision floating-point format. A tensor operation can be performed using the converted input tensor. A result of the tensor operation can be converted from the block floating-point format to the normal-precision floating-point format. The converted result can be used to generate an output tensor of the layer of the neural network, where the output tensor is in normal-precision floating-point format.

Abstract: Technology related to training a neural network accelerator using mixed precision data formats is disclosed. In one example of the disclosed technology, a neural network accelerator is configured to accelerate a given layer of a multi-layer neural network. An input tensor for the given layer can be converted from a normal-precision floating-point format to a quantized-precision floating-point format. A tensor operation can be performed using the converted input tensor. A result of the tensor operation can be converted from the block floating-point format to the normal-precision floating-point format. The converted result can be used to generate an output tensor of the layer of the neural network, where the output tensor is in normal-precision floating-point format.

Abstract: A method for hardware-based machine learning acceleration is provided. The method may include partitioning, into a first batch of data and a second batch of data, an input data received at a hardware accelerator implementing a machine learning model. The input data may be a continuous stream of data samples. The input data may be partitioned based at least on a resource constraint of the hardware accelerator. An update of a probability density function associated with the machine learning model may be performed in real time. The probability density function may be updated by at least processing, by the hardware accelerator, the first batch of data before the second batch of data. An output may be generated based at least on the updated probability density function. The output may include a probability of encountering a data value. Related systems and articles of manufacture, including computer program products, are also provided.

Abstract: Methods and apparatus are disclosed supporting a design flow for developing quantized neural networks. In one example of the disclosed technology, a method includes quantizing a normal-precision floating-point neural network model into a quantized format. For example, the quantized format can be a block floating-point format, where two or more elements of tensors in the neural network share a common exponent. A set of test input is applied to a normal-precision flooding point model and the corresponding quantized model and the respective output tensors are compared. Based on this comparison, hyperparameters or other attributes of the neural networks can be adjusted. Further, quantization parameters determining the widths of data and selection of shared exponents for the block floating-point format can be selected. An adjusted, quantized neural network is retrained and programmed into a hardware accelerator.

Abstract: A method, a system, and a computer program product for fast training and/or execution of neural networks. A description of a neural network architecture is received. Based on the received description, a graph representation of the neural network architecture is generated. The graph representation includes one or more nodes connected by one or more connections. At least one connection is modified. Based on the generated graph representation, a new graph representation is generated using the modified at least one connection. The new graph representation has a small-world property. The new graph representation is transformed into a new neural network architecture.

Abstract: Embodiments of the present disclosure include systems and methods for sparsifying narrow data formats for neural networks. A plurality of activation values in a neural network are provided to a muxing unit. A set of sparsification operations are performed on a plurality of weight values to generate a subset of the plurality of weight values and mask values associated with the plurality of weight values. The subset of the plurality of weight values are provided to a matrix multiplication unit. The muxing unit generates a subset of the plurality of activation values based on the mask values and provides the subset of the plurality of activation values to the matrix multiplication unit. The matrix multiplication unit performs a set of matrix multiplication operations on the subset of the plurality of weight values and the subset of the plurality of activation values to generate a set of outputs.

Abstract: A method for detecting and/or preventing an adversarial attack against a target machine learning model may be provided. The method may include training, based at least on training data, a defender machine learning model to enable the defender machine learning model to identify malicious input samples. The trained defender machine learning model may be deployed at the target machine learning model. The trained defender machine learning model may be coupled with the target machine learning model to at least determine whether an input sample received at the target machine learning model is a malicious input sample and/or a legitimate input sample. Related systems and articles of manufacture, including computer program products, are also provided.

Abstract: Embodiments of the present disclosure include systems and methods for performing data-aware model pruning for neural networks. During a training phase, a neural network is trained with a first set of data. During a validation phase, inference with the neural network is performed using a second set of data that causes the neural network to generate a first set of outputs at a layer in the neural network. During the validation phase, a plurality of mean values and a plurality of variance values are calculated based on the first set of outputs. A plurality of entropy values are calculated based on the plurality of mean values and the plurality of variance values. A second set of outputs are pruned based on the plurality of entropy values. The second set of outputs are generated by the layer of the neural network using a third set of data.

Abstract: Embodiments of the present disclosure includes systems and methods for reducing computational cost associated with training a neural network model. A neural network model is received and a neural network training process is executed in which the neural network model is trained according to a first fidelity during a first training phase. As a result of a determination that training of the neural network model during the first training phase satisfies one or more criteria, the neural network model is trained at a second fidelity during a second training phase, the second fidelity being a higher fidelity than the first fidelity.