Abstract: In one embodiment, a computing device includes an input sensor providing an input data; a programmable logic device (PLD) implementing a convolutional neural network (CNN), wherein: each compute block of the PLD corresponds to one of a multiple of convolutional layers of the CNN, each compute block of the PLD is placed in proximity to at least two memory blocks, a first one of the memory blocks serves as a buffer for the corresponding layer of the CNN, and a second one of the memory blocks stores model-specific parameters for the corresponding layer of the CNN.

Abstract: Embodiments relate to streaming convolution operations in a neural processor circuit that includes a neural engine circuit and a neural task manager. The neural task manager obtains multiple task descriptors and multiple subtask descriptors. Each task descriptor identifies a respective set of the convolution operations of a respective layer of a set of layers. Each subtask descriptor identifies a corresponding task descriptor and a subset of the convolution operations on a portion of a layer of the set of layers identified by the corresponding task descriptor. The neural processor circuit configures the neural engine circuit for execution of the subset of the convolution operations using the corresponding task descriptor. The neural engine circuit performs the subset of the convolution operations to generate output data that correspond to input data of another subset of the convolution operations identified by another subtask descriptor from the list of subtask descriptors.

Abstract: Embodiments relate to streaming operations in a neural processor circuit that includes a neural engine circuit and a data processor circuit. The neural engine circuit performs first operations on a first input tensor of a first layer to generate a first output tensor, and second operations on a second input tensor of a second layer at a higher hierarchy than the first layer, the second input tensor corresponding to the first output tensor. The data processor circuit stores a portion of the first input tensor for access by the neural engine circuit to perform a subset of the first operations and generate a portion of the first output tensor. The data processor circuit stores the portion of the first output tensor for access by the neural engine circuit as a portion of the second input tensor to perform a subset of the second operations.

Abstract: Systems and processes for training and compressing a convolutional neural network model include the use of quantization and layer fusion. Quantized training data is passed through a convolutional layer of a neural network model to generate convolutional results during a first iteration of training the neural network model. The convolutional results are passed through a batch normalization layer of the neural network model to update normalization parameters of the batch normalization layer. The convolutional layer is fused with the batch normalization layer to generate a first fused layer and the fused parameters of the fused layer are quantized. The quantized training data is passed through the fused layer using the quantized fused parameters to generate output data, which may be quantized for a subsequent layer in the training iteration.

Abstract: In one embodiment, a computing device includes an input sensor providing an input data; a programmable logic device (PLD) implementing a convolutional neural network (CNN), wherein: each compute block of the PLD corresponds to one of a multiple of convolutional layers of the CNN, each compute block of the PLD is placed in proximity to at least two memory blocks, a first one of the memory blocks serves as a buffer for the corresponding layer of the CNN, and a second one of the memory blocks stores model-specific parameters for the corresponding layer of the CNN.

Abstract: In one embodiment, a computing device includes an input sensor providing an input data; a programmable logic device (PLD) implementing a convolutional neural network (CNN), wherein: each compute block of the PLD corresponds to one of a multiple of convolutional layers of the CNN, each compute block of the PLD is placed in proximity to at least two memory blocks, a first one of the memory blocks serves as a buffer for the corresponding layer of the CNN, and a second one of the memory blocks stores model-specific parameters for the corresponding layer of the CNN.

Abstract: Examples described herein include systems, devices, and methods for backscattering carrier signals in accordance with pixel values of an image and/or video. Signals having a property proportionate to pixel values may be converted into a pulse-containing waveform having pulses whose widths and/or duty cycles are determined based on the pixel values. Backscatter transmitters may backscatter a carrier signal in accordance with the pulse-containing waveform to provide the pixel values to a receiver. In this manner, video transmission at low power and/or battery-free operation may be provided.

Abstract: Examples described herein include systems, devices, and methods for backscattering carrier signals in accordance with pixel values of an image and/or video. Signals having a property proportionate to pixel values may be converted into a pulse-containing waveform having pulses whose widths and/or duty cycles are determined based on the pixel values. Backscatter transmitters may backscatter a carrier signal in accordance with the pulse-containing waveform to provide the pixel values to a receiver. In this manner, video transmission at low power and/or battery-free operation may be provided.

Abstract: In one embodiment, a computing device includes an input sensor providing an input data; a programmable logic device (PLD) implementing a convolutional neural network (CNN), wherein: each compute block of the PLD corresponds to one of a multiple of convolutional layers of the CNN, each compute block of the PLD is placed in proximity to at least two memory blocks, a first one of the memory blocks serves as a buffer for the corresponding layer of the CNN, and a second one of the memory blocks stores model-specific parameters for the corresponding layer of the CNN.

Abstract: Systems and processes for training and compressing a convolutional neural network model include the use of quantization and layer fusion. Quantized training data is passed through a convolutional layer of a neural network model to generate convolutional results during a first iteration of training the neural network model. The convolutional results are passed through a batch normalization layer of the neural network model to update normalization parameters of the batch normalization layer. The convolutional layer is fused with the batch normalization layer to generate a first fused layer and the fused parameters of the fused layer are quantized. The quantized training data is passed through the fused layer using the quantized fused parameters to generate output data, which may be quantized for a subsequent layer in the training iteration.

Abstract: Examples described herein include wireless transmitters configured for power transmission. Example wireless transmitters may insert power packets into wireless communications such that power harvesting circuitry may harvest sufficiently continuous power from the wireless communication signals. Example power harvesting circuitry is configured to harvest power across multiple wireless communication channels. Example chargers are described which may harvest power from wireless communication signals (e.g. Wi-Fi signals).

Abstract: Examples described herein include wireless transmitters configured for power transmission. Example wireless transmitters may insert power packets into wireless communications such that power harvesting circuitry may harvest sufficiently continuous power from the wireless communication signals. Example power harvesting circuitry is configured to harvest power across multiple wireless communication channels. Example chargers are described which may harvest power from wireless communication signals (e.g. Wi-Fi signals).