Heterogeneous Neural Processing Units for Computational Efficiency
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Solution Overview
Problem
Conventional neural network processor systems suffer from efficiency and effectiveness issues when executing various neural network applications due to their homogeneous design, where each neural processing core has the same structural configuration, leading to suboptimal performance and resource utilization.
Innovation Solution
A neural network processor system with heterogeneous neural processing units, each having a different structural configuration, is introduced, where a central processing unit coordinates the units to assign tasks based on their specific configurations, optimizing task distribution and improving computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If homogeneous neural processing units are used, then device complexity is reduced and ease of manufacture is improved, but productivity and computational efficiency deteriorate
Solution Approach 1:
The system is segmented into multiple neural processing units with different structural configurations, each optimized for specific computational patterns. This segmentation allows the system to handle diverse neural network workloads more efficiently while maintaining manufacturing feasibility through modular design approaches.
Solution Approach 2:
Different neural processing units are assigned different structural qualities and configurations tailored to their specific functional requirements. Some units may have more processing cores for computationally intensive layers, while others have optimized memory structures for data-intensive operations, achieving local optimization without requiring complete system redesign.
2Device complexity
If homogeneous neural processing units are used, then device complexity is reduced, but adaptability to different neural network architectures deteriorates
Solution Approach 1:
The system employs neural processing units with varying structural parameters such as number of processing cores, memory size, and interconnect topology. These parameter variations enable the system to adapt to different neural network architectures and workloads while maintaining a fundamentally similar design framework that doesn't excessively increase complexity.
Solution Approach 2:
Each neural processing unit is designed with universal interfaces and control mechanisms that allow them to perform multiple functions despite having different structural configurations. The central controller can dynamically allocate and configure units for different computational tasks, achieving versatility without requiring completely specialized hardware for each function.
3Productivity
If heterogeneous neural processing units are used, then productivity and computational efficiency are improved, but device complexity increases
Solution Approach 1:
A central controller acts as an intermediary between the heterogeneous neural processing units and the external system. This intermediary manages the complexity by providing a unified interface for task allocation, resource management, and coordination, allowing the heterogeneous units to work together efficiently without requiring complex point-to-point control logic between each unit.
Solution Approach 2:
The system employs dynamic configuration and allocation mechanisms that allow the heterogeneous neural processing units to be adaptively assigned to different computational tasks based on real-time workload characteristics. This dynamic approach maximizes productivity by matching unit capabilities to task requirements while managing complexity through software-based control rather than hardwired configurations.
Data Source
AI summary
There is provided a neural network processor system including: a plurality of neural processing units, including a first neural processing unit and a second neural processing unit, whereby each neural processing unit comprises an array of neural processing core blocks, each neural processing core block comprising a neural processing core; and at least one central processing unit communicatively coupled to the plurality of neural processing units and configured to coordinate the plurality of neural processing units for performing neural network computations. In particular, the first and second neural processing units have a different structural configuration to each other. There is also provided a corresponding method of operating and a corresponding method of forming the neural network processor system.