Abstract: Artificial intelligence is an increasingly important sector of the computer industry. However, artificial intelligence is extremely computationally intensive field such that it can be expensive, time consuming, and energy consuming. Fortunately, many of the calculations required for artificial intelligence can be performed in parallel such that specialized processors can greatly increase computational performance. Specifically, artificial intelligence generally requires large numbers of matrix operations to implement neural networks such that specialized Matrix Processor circuits can improve performance. But a neural network is more than a collection of matrix operations; it is a set of specifically coordinated matrix operations with complex data dependencies. Without proper coordination, Matrix Processor circuits may end up idle or spending large amounts of time loading in different weight matrix data.

Abstract: Artificial intelligence is an extremely computationally intensive field such that it can be expensive, time consuming, and energy consuming. Fortunately, many of the calculations required for artificial intelligence can be performed in parallel such that specialized processors can greatly increase computational performance. Specifically, artificial intelligence generally requires a large flow of data from different types of memory. To maximize the process of a multilayer neural network, the reordering of data onto and out of a neural network processor, the computations by the matrix of processing elements within the neural network processor, and the synchronization of these activities are reordered.

Abstract: Artificial intelligence is an increasingly important sector of the computer industry. However, artificial intelligence is very computationally intensive field. Fortunately, many of the required calculations can be performed in parallel such that specialized processors can greatly increase computation performance. In particular, Graphics Processor Units (GPUs) are often used in artificial intelligence. Although GPUs have helped, they are not ideal for artificial intelligence. Specifically, GPUs are used to compute matrix operations in one direction with a pipelined architecture. However, artificial intelligence is a field that uses both forward propagation computations and back propagation calculations. To efficiently perform artificial intelligence calculations, a symmetric matrix processing element is introduced. The symmetric matrix processing element can perform forward propagation and backward propagation calculations just as easily.

Abstract: Artificial intelligence is an increasingly important sector of the computer industry. One of the most important applications for artificial intelligence is object recognition and classification from digital images. Convolutional neural networks have proven to be a very effective tool for object recognition and classification from digital images. However, convolutional neural networks are extremely computationally intensive thus requiring high-performance processors, significant computation time, and significant energy consumption. To reduce the computation time and energy consumption a “cone of dependency” and “cone of influence” processing techniques are disclosed. These two techniques arrange the computations required in a manner that minimizes memory accesses such that computations may be performed in local cache memory. These techniques significantly reduce the time to perform the computations and the energy consumed by the hardware implementing a convolutional neural network.

Abstract: Artificial intelligence is an increasingly important sector of the computer industry. However, artificial intelligence is very computationally intensive field. Fortunately, many of the required calculations can be performed in parallel such that specialized processors can greatly increase computation performance. In particular, Graphics Processor Units (GPUs) are often used in artificial intelligence. Although GPUs have helped, they are not ideal for artificial intelligence. Specifically, GPUs are used to compute matrix operations in one direction with a pipelined architecture. However, artificial intelligence is a field that uses both forward propagation computations and back propagation calculations. To efficiently perform artificial intelligence calculations, a symmetric matrix processing element is introduced. The symmetric matrix processing element can perform forward propagation and backward propagation calculations just as easily.

Abstract: Artificial intelligence is an increasingly important sector of the computer industry. However, artificial intelligence is extremely computationally intensive field such that it can be expensive, time consuming, and energy consuming field. Fortunately, many of the calculations required for artificial intelligence can be performed in parallel such that specialized processors can great increase computational performance for AI applications. Specifically, artificial intelligence generally requires large numbers of matrix operations such that specialized matrix processor circuits can greatly improve performance. To efficiently execute all these matrix operations, the matrix processor circuits must be quickly and efficiently supplied with a stream of data and instructions to process or else the matrix processor circuits end up idle. Thus, this document discloses packet architecture for efficiently creating and supplying neural network processors with work packets to process.

Abstract: Artificial intelligence is an increasingly important sector of the computer industry. One of the most important applications for artificial intelligence is object recognition and classification from digital images. Convolutional neural networks have proven to be a very effective tool for object recognition and classification from digital images. However, convolutional neural networks are extremely computationally intensive thus requiring high-performance processors, significant computation time, and significant energy consumption. To reduce the computation time and energy consumption a “cone of dependency” and “cone of influence” processing techniques are disclosed. These two techniques arrange the computations required in a manner that minimizes memory accesses such that computations may be performed in local cache memory. These techniques significantly reduce the time to perform the computations and the energy consumed by the hardware implementing a convolutional neural network.

Abstract: Artificial intelligence is an increasingly important sector of the computer industry. One of the most important applications for artificial intelligence is object recognition and classification from digital images. Convolutional neural networks have proven to be a very effective tool for object recognition and classification from digital images. However, convolutional neural networks are extremely computationally intensive thus requiring high-performance processors, significant computation time, and significant energy consumption. To reduce the computation time and energy consumption a “cone of dependency” and “cone of influence” processing techniques are disclosed. These two techniques arrange the computations required in a manner that minimizes memory accesses such that computations may be performed in local cache memory. These techniques significantly reduce the time to perform the computations and the energy consumed by the hardware implementing a convolutional neural network.

Abstract: Artificial intelligence is an increasingly important sector of the computer industry. However, artificial intelligence is an extremely computationally intensive field such that performing artificial intelligence calculations can be expensive, time consuming, and energy consuming. Fortunately, many of the calculations required for artificial intelligence applications can be performed in parallel such that specialized linear algebra matrix processors can greatly increase computational performance. But even with linear algebra matrix processors; performance can be limited due to complex data dependencies. Without proper coordination, linear algebra matrix processors may end up idle or spending large amounts of time moving data around. Thus, this document discloses methods for efficiently scheduling linear algebra matrix processors.

Abstract: Artificial intelligence is an increasingly important sector of the computer industry. However, artificial intelligence is extremely computationally intensive field such that it can be expensive, time consuming, and energy consuming. Fortunately, many of the calculations required for artificial intelligence can be performed in parallel such that specialized processors can great increase computational performance. Specifically, artificial intelligence generally requires large numbers of matrix operations to implement neural networks such that specialized Matrix Processor circuits can improve performance. But a neural network is more than a collection of matrix operations; it is a set of specifically coordinated matrix operations with complex data dependencies. Without proper coordination, Matrix Processor circuits may end up idle or spending large amounts of time loading in different weight matrix data.

Abstract: Artificial intelligence is an increasingly important sector of the computer industry. However, artificial intelligence is very computationally intensive field. Fortunately, many of the required calculations can be performed in parallel such that specialized processors can great increase computation performance. In particular, Graphics Processor Units (GPUs) are often used in artificial intelligence. Although GPUs have helped, they are not ideal for artificial intelligence. Specifically, GPUs are used to compute matrix operations in one direction with a pipelined architecture. However, artificial intelligence is a field that uses both forward propagation computations and back propagation calculations. To efficiently perform artificial intelligence calculations, a symmetric matrix processing element is introduced. The symmetric matrix processing element can perform forward propagation and backward propagation calculations just as easily.

Abstract: Provided are a method, a non-transitory computer-readable storage device and an apparatus for managing use of a shared memory buffer that is partitioned into multiple banks and that stores incoming data received at multiple inputs in accordance with a multi-slice architecture. A particular bank is allocated to a corresponding slice. Received respective data packets are associated with corresponding slices based on which respective inputs they are received. Determine, based on a state of the shared memory buffer, to transfer contents of all occupied cells of the particular bank. Writes to the bank are stopped, contents of occupied cells are transferred to cells of one or more other banks associated with the particular bank's slice, information is stored indicating where the contents have been transferred, and the particular bank is returned to a shared pool after transferring is completed.

Abstract: Provided are a method, a non-transitory computer-readable storage device and an apparatus for managing use of a shared memory buffer that is partitioned into multiple banks and that stores incoming data received at multiple inputs in accordance with a multi-slice architecture. A particular bank is allocated to a corresponding slice. Received respective data packets are associated with corresponding slices based on which respective inputs they are received. Determine, based on a state of the shared memory buffer, to transfer contents of all occupied cells of the particular bank. Writes to the bank are stopped, contents of occupied cells are transferred to cells of one or more other banks associated with the particular bank's slice, information is stored indicating where the contents have been transferred, and the particular bank is returned to a shared pool after transferring is completed.