Multi-Agent Instruction Engine for Conflict-Free Neural Inference
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Solution Overview
Problem
Existing neural inference processing systems face challenges in efficiently exploiting algorithmic parallelism, particularly in terms of pipeline and data path parallelism, leading to suboptimal performance in neural network computations.
Innovation Solution
A multi-agent instruction execution engine for neural inference processing, which utilizes a neural core with a shared functional unit architecture, separate program counters for each agent, and static scheduling to ensure conflict-free execution of multiple instruction streams concurrently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple instruction streams are executed concurrently to exploit algorithmic parallelism, then processing speed and productivity are improved, but resource conflicts and execution conflicts may occur
Solution Approach 1:
The patent applies static scheduling at compile time to pre-determine the execution timeline of multiple instruction streams. This preliminary action assigns specific time slots and functional units to each instruction before runtime, ensuring that conflicting operations are detected and resolved in advance, thus enabling concurrent execution without runtime conflicts.
Solution Approach 2:
The patent segments the instruction execution into distinct time slots and assigns different instruction streams to different functional units at different times. By dividing the execution space-time resource into discrete segments, the system allows multiple instruction streams to execute concurrently without interfering with each other, thus improving productivity while maintaining reliability.
2Device complexity
If shared functional units are used to improve resource utilization, then device complexity is reduced, but execution conflicts between multiple agents increase
Solution Approach 1:
The patent resolves the conflict between shared functional units and execution reliability by performing static scheduling at compile time. The scheduler analyzes all instruction streams and assigns specific functional units and time slots to each operation in advance, eliminating runtime conflicts while maintaining shared resource architecture. This preliminary planning enables high resource utilization without sacrificing execution reliability.
3Adaptability or versatility
If separate program counters are maintained for each agent to enable independent execution, then adaptability and versatility are improved, but device complexity increases
Solution Approach 1:
The patent implements a universal program counter design that serves multiple agents simultaneously. Rather than implementing completely separate control structures for each agent, the system uses a single program counter that is statically configured to manage multiple instruction streams. This multi-functional approach provides the adaptability of independent agent execution while avoiding the complexity of multiple separate control units.
Data Source
AI summary
Multi-agent instruction execution engines for neural inference processing are provided. In various embodiments, a neural core is provided. The neural core includes an instruction memory. The instruction memory comprises a plurality of instruction streams, each instruction stream associated with one of a plurality of agents. The instruction memory further comprises a plurality of shared functional units. The neural core is adapted to concurrently execute the plurality of instruction streams on the plurality of associated agents. The execution includes maintaining a separate program counter for each of the plurality of agents, determining a plurality of operations from the instructions of each instruction stream, and directing the operations to the shared functional units. The instructions of each instruction stream are statically scheduled prior to runtime to ensure their execution is conflict free.