Distributed Tile CPU Control for Parallel ML Acceleration
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Solution Overview
Problem
Data processing systems with central processing units (CPUs) face inefficiencies in handling computationally-intensive tasks like machine learning due to the need for centralized control of hardware accelerators, leading to performance overheads and resource competition.
Innovation Solution
A cluster of compute tiles, each with a tile CPU and a hardware accelerator, allows for parallel processing and decentralized control of accelerators, reducing overheads and improving performance by using a dedicated interface and shared memory management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If centralized control of hardware accelerators is used, then resource management is simplified, but performance overhead increases and resource competition occurs
Solution Approach 1:
The system divides the centralized accelerator control into distributed tile-level control units. Each compute tile has its own accelerator interface and can independently manage accelerator resources, eliminating the single-point bottleneck and enabling parallel task execution across multiple tiles without resource contention.
Solution Approach 2:
The patent introduces a new hierarchical dimension by adding tile-level intermediate control between the central processor and accelerators. This multi-level control structure allows simultaneous local autonomy at tile level while maintaining global coordination, resolving the trade-off between control simplicity and execution efficiency.
2Productivity
If parallel processing is implemented across multiple accelerators, then processing efficiency improves, but system complexity increases
Solution Approach 1:
The system segments the parallel processing architecture into identical, independent compute tiles. Each tile contains a complete set of processing resources (CPU cores, accelerators, memory), allowing parallel execution with minimal inter-tile coordination. This modular segmentation reduces system complexity by making each tile self-sufficient while enabling scalable parallelism.
Solution Approach 2:
The patent changes the organizational parameter from centralized-hierarchical to distributed-parallel by configuring multiple identical compute tiles to operate simultaneously. This parameter change enables linear scaling of processing efficiency with the number of tiles while maintaining consistent per-tile complexity, effectively decoupling system complexity from processing capacity.
3Productivity
If dedicated accelerator interfaces are provided at each compute tile, then resource utilization is optimized, but device complexity increases
Solution Approach 1:
The compute tile design uses universal, identical interfaces and resource structures across all tiles. Each tile implements the same accelerator interface specification, allowing the system to achieve high resource utilization through parallelism without increasing per-tile complexity. The universality principle ensures that adding more tiles does not proportionally increase overall system complexity.
Data Source
AI summary
A data processing method comprises:obtaining at a cluster CPU, a request to perform a machine learning process; andcoordinating a plurality of tile CPUs to participate in performing the machine learning process, whereinthe tile CPUs participate in performing the machine learning process by delegating asynchronous tasks to an accelerator attached to each respective tile CPU.