Abstract: Examples herein relate to a solid state drive that includes a media, first circuitry, and second circuitry. In some examples, the first circuitry is to execute one or more commands. In some examples, the second circuitry is to receive a configuration of at one type of command, where the configuration is to define an amount of media bandwidth allocated for the at one type of command; receive a command; and assign the received command to the first circuitry for execution.

Abstract: A processor architecture and model exploration system for deep learning is provided. A method of improving performance of a processor system and associated software includes selecting a set of performance parameter targets for a processor architecture having a set of functional units and an AI model. The method also includes evaluating performance of the processor architecture and the AI model and adjusting at least one of the functional units of the processor architecture to form a new processor architecture prior to iteratively evaluating the combination of the new processor architecture and the AI model. Further, the method includes repeating the evaluating step and the adjustment step until the performance evaluation of the processor architecture and AI model meets the set of performance parameter targets.

Abstract: A system and method of generating an efficient neural network model architecture and an efficient processor for deep learning in an artificial intelligence (AI) processor are provided. The system and method to create the processor architecture as a companion to the neural network model by composing a plurality of processor architectures to enable architectural exploration. The compilation can be implemented for any arbitrary spatial processor architecture using either ASIC or FPGA devices. The processor architecture can be uniquely defined for a selected ML or AI model without having to update the software compiler.

Abstract: Examples herein relate to a solid state drive that includes a media, first circuitry, and second circuitry. In some examples, the first circuitry is to execute one or more commands. In some examples, the second circuitry is to receive a configuration of at one type of command, where the configuration is to define an amount of media bandwidth allocated for the at one type of command; receive a command; and assign the received command to the first circuitry for execution.

Abstract: Systems and methods for dynamically sizing inter-kernel communication channels implemented on an integrated circuit (IC) are provided. Implementation characteristics of the channels, predication, and kernel scheduling imbalances may factor into properly sizing the channels for self-synchronization, resulting in optimized steady-state throughput.

Abstract: A graph partitioning compiler partitions an AI program or model for execution on multiple TSP modules configured for accelerating deep learning workloads.

Abstract: Examples herein relate to a solid state drive that includes a media, first circuitry, and second circuitry. In some examples, the first circuitry is to execute one or more commands. In some examples, the second circuitry is to receive a configuration of at one type of command, where the configuration is to define an amount of media bandwidth allocated for the at one type of command; receive a command; and assign the received command to the first circuitry for execution.

Abstract: Examples herein relate to a solid state drive that includes a media, a processing system, and a media command arbiter configured to permit execution of a specific allocation of storage and compute commands based on a configuration, wherein the media command arbiter is to transfer commands to the media based on the configuration. The media can be locally connected to a compute engine processing system that is configurable to perform computations on data stored in the media. The configuration can indicate a number of compute commands and storage commands that are permitted to be performed over a period of time or media bandwidth allocated to compute commands and storage commands. The processing system can include an inference engine that performs one or more of: data pattern recognition, image recognition, augmented reality overlay applications, face recognition, object recognition, or voice recognition, language translation.

Abstract: Systems and methods for dynamically sizing inter-kernel communication channels implemented on an integrated circuit (IC) are provided. Implementation characteristics of the channels, predication, and kernel scheduling imbalances may factor into properly sizing the channels for self-synchronization, resulting in optimized steady-state throughput.

Abstract: A method for implementing a convolutional neural network (CNN) accelerator on a target includes utilizing one or more processing elements to perform convolution. A configuration of the CNN accelerator is modified to change filters implemented by the CNN accelerator and to change formatting of output data. The one or more processing elements are utilized to perform one of deconvolution and backpropagation convolution in response to the change in the filters and formatting of the output data.

Abstract: Systems and methods for dynamically sizing inter-kernel communication channels implemented on an integrated circuit (IC) are provided. Implementation characteristics of the channels, predication, and kernel scheduling imbalances may factor into properly sizing the channels for self-synchronization, resulting in optimized steady-state throughput.

Abstract: A method for implementing a convolutional neural network (CNN) accelerator on a target includes utilizing one or more processing elements to implement a standard convolution layer. A configuration of the CNN accelerator is modified to change a data flow between components on the CNN accelerator. The one or more processing elements is utilized to implement a fully connected layer in response to the change in the data flow.

Abstract: A method for implementing a convolutional neural network (CNN) accelerator on a target includes identifying characteristics and parameters for the CNN accelerator. Resources on the target are identified. A design for the CNN accelerator is generated in response to the characteristics and parameters of the CNN accelerator and the resources on the target.

Abstract: A method for implementing a convolutional neural network (CNN) accelerator on a target includes utilizing one or more processing elements to implement a standard convolution layer. A configuration of the CNN accelerator is modified to change a data flow between components on the CNN accelerator. The one or more processing elements is utilized to implement a fully connected layer in response to the change in the data flow.

Abstract: Examples herein relate to a solid state drive that includes a media, a processing system, and a media command arbiter configured to permit execution of a specific allocation of storage and compute commands based on a configuration, wherein the media command arbiter is to transfer commands to the media based on the configuration. The media can be locally connected to a compute engine processing system that is configurable to perform computations on data stored in the media. The configuration can indicate a number of compute commands and storage commands that are permitted to be performed over a period of time or media bandwidth allocated to compute commands and storage commands. The processing system can include an inference engine that performs one or more of: data pattern recognition, image recognition, augmented reality overlay applications, face recognition, object recognition, or voice recognition, language translation.

Abstract: A method for implementing a convolutional neural network (CNN) accelerator on a target includes utilizing one or more processing elements to implement a standard convolution layer. A configuration of the CNN accelerator is modified to change a data flow between components on the CNN accelerator. The one or more processing elements is utilized to implement a fully connected layer in response to the change in the data flow.

Abstract: Systems and methods for calculating a dot product using digital signal processing units that are organized into a dot product processing unit for dot product processing using multipliers and adders of the digital signal processing units.

Abstract: Systems and methods for generating and deploying integrated circuit (IC) applications are provided. Global variable implementation logic may be used to optimize implementation, on an integrated circuit, of functionality represented by high-level code including global variables. A compiler's intermediate representation is analyzed for one or more characteristics that may be used to determine one or more initialization parameters, one or more scope parameters, one or more implementation parameters, or any combination thereof of the functionality. An HDL is generated based upon the one or more initialization parameters, the one or more scope parameters, the one or more implementation parameters, or the any combination thereof.

Abstract: Devices and methods for filtering data include calculating intermediate input values from input elements using a transformation function. The transformation function is based at least in part on a size of the filter and a number of filter outputs. Intermediate filter values are calculated from filter elements of the filter using the transformation function. Each intermediate input value is multiplied with a respective intermediate filter value to form intermediate values. These intermediate values are combined with each other using the transformation function to determine one or more output values.

Abstract: Systems and methods for calculating a dot product using digital signal processing units that are organized into a dot product processing unit for dot product processing using multipliers and adders of the digital signal processing units.