Neural Processor Task Context Switch Circuit
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Solution Overview
Problem
Existing neural processor circuits face challenges in efficiently managing context switches between different tasks, leading to increased power consumption and bandwidth usage, especially when handling multiple neural networks simultaneously.
Innovation Solution
A neural processor circuit is designed with a task manager circuit that facilitates context switches by sending configuration data to a data processor circuit, allowing it to transfer output data from one task to external system memory and fetch data for an incoming task, thereby optimizing buffer usage and reducing power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a CPU and main memory are used to execute machine learning systems, then ease of configuration is improved, but power consumption and CPU bandwidth increase significantly
Solution Approach 1:
The system divides the machine learning execution into two segments: a CPU that handles high-level task management and configuration, and a dedicated neural processor that handles computational operations. This segmentation allows the CPU to remain idle during computation, significantly reducing overall power consumption while maintaining ease of configuration through the task manager interface.
Solution Approach 2:
A task manager circuit acts as an intermediary between the CPU and the neural processor. It receives tasks from the CPU, manages context switches between different neural network operations, and coordinates data transfer. This intermediary layer enables the CPU to configure multiple neural networks without continuously executing their computational operations, reducing power consumption while maintaining configuration flexibility.
2Productivity
If multiple neural networks are processed simultaneously on a single CPU, then productivity is improved, but power consumption and bandwidth usage increase
Solution Approach 1:
The neural processor is designed as a universal computing unit that can execute multiple different neural network operations simultaneously through parallel hardware units. The task manager coordinates these universal units to handle different neural networks concurrently, improving overall productivity while the dedicated hardware nature of the neural processor keeps power consumption lower than running all operations on a general-purpose CPU.
3Adaptability or versatility
If context switching between tasks is performed frequently, then adaptability is improved, but power consumption and bandwidth usage increase
Solution Approach 1:
The task manager performs preliminary actions by pre-loading task configurations and data into the neural processor before actual computation begins. When context switching is needed, the system can quickly transition between pre-prepared tasks without performing complex real-time reconfiguration operations, reducing the power consumption associated with frequent context switching while maintaining high adaptability.
Data Source
AI summary
A neural processor includes neural engines for performing convolution operations on input data corresponding to one or more tasks to generate output data. The neural processor also includes a data processor circuit coupled to external system memory. The data processor circuit includes a buffer for storing the output data from the neural engines. The neural processor further includes a task manager coupled to the data processor circuit. The task manager receives a context-switch task. The context-switch task specifies a switch of the data processor circuit from handling an outgoing task to an incoming task. The task manager sends configuration data of the context-switch task to cause the data processor circuit to transmit the output data corresponding to the outgoing task from the buffer to the external system memory. The data processor circuit also fetches data corresponding to the incoming task from the external system memory to the buffer.