Apple Inc.

Sayyed Karen Khatamifard

Saman Naderiparizi

Mohammad Rastegari

Hessam Bagherinezhad

Anish Jnyaneshwar Prabhu

Rohit Kumar Gupta

Ramana V Rachakonda

David S Bakin

Sophie Lebrecht

Andrew Luke Chronister

Alon Yaakov

Jonathan Gelsey

Husam Khashiboun

Andrew Z Luo

Jeffrey D Marker

James C Gabriel

Chenfan Sun

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.

Customizable chip for AI applications

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.

Subtask storage for streaming convolutions in neural network processor

Aspects of the subject technology relate to machine learning based object recognition using pixel difference information. A difference image generated by subtraction of a current image from one or more previous images can be provided, as input, to a machine-learning engine. The machine-learning may output a detected object or a detected action based, at least in part, on the difference image. In this way, temporal information about the object can be provided to, and used by, a machine-learning model that is structured to accept image input.

Object detection based on pixel differences

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.

Compressed convolutional neural network models

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Seattle, WA, United States of America

Sayyed Karen Khatamifard