GPU ML Cluster Layout With Adjacent SRAM for Lower Data Latency
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Solution Overview
Problem
Existing GPU architectures face challenges in improving power efficiency of machine learning arithmetic units (ML ALUs) while managing the area cost and data delivery latency, which are significant consumers of GPU power.
Innovation Solution
Implementing ML clusters with separate voltage domains, reducing data access latency by positioning them adjacent to SRAM, and utilizing scratchpad memory to minimize data delivery power and latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If separate voltage domains are used for ML ALUs, then power efficiency is improved, but area cost increases
Solution Approach 1:
The GPU is divided into separate voltage domains, with ML ALUs operating in a first voltage domain and other components in a second voltage domain. This segmentation allows independent power management, enabling the ML ALUs to receive higher voltage when needed for intensive computations while other components operate at lower voltages, thereby improving overall power efficiency without requiring the entire GPU to consume excessive power.
Solution Approach 2:
Different voltage levels are applied to different parts of the GPU based on their specific computational needs. The ML ALUs receive higher voltage domains during intensive operations, while non-ML components operate at lower voltage domains. This local quality approach optimizes power efficiency by matching power delivery to actual computational demand in each region, reducing wasted energy across the entire device.
2Loss of time
If ML clusters are positioned adjacent to memory modules, then data access latency is reduced, but device complexity increases
Solution Approach 1:
ML clusters are merged with memory modules by positioning them adjacent to each other on the GPU die. This physical merging creates a tightly coupled memory-compute architecture where data can be accessed directly between the ML ALUs and memory modules without traversing long distances through the interconnect fabric, significantly reducing data access latency for machine learning workloads.
Solution Approach 2:
A memory controller acts as an intermediary between the ML clusters and memory modules, managing data flow and access patterns. This intermediary coordinates the high-speed data transfer between adjacent memory modules and ML ALUs, enabling the system to achieve low latency while maintaining architectural organization and managing the complexity of the tightly coupled design.
Data Source
AI summary
A graphics processing unit (GPU) for clustering of machine learning (ML) functional components, including: a plurality of compute units; a plurality of ML clusters, wherein each of the ML clusters comprises at least one arithmetic logic unit (ALU), and wherein each of the ML clusters is associated with a respective subset of the compute units; and a plurality of memory modules each positioned on the GPU adjacent to a respective ML cluster of the plurality of ML clusters, wherein each ML cluster is configured to directly access one or more adjacent memory modules.