Distributed ML Processing Units with HBM Stacks for Latency Reduction
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Solution Overview
Problem
Current machine learning model processing systems face challenges with scalability and efficiency due to high latency and inefficient data processing, particularly in large language models and transformer architectures.
Innovation Solution
The system integrates high-bandwidth memory (HBM) stacks with compute layers in a single stack on each processing unit, enabling localized execution of machine learning model computations. This configuration, combined with interposer connectivity for high-speed communication, allows for distributed processing and synchronization of computations across multiple processing units.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If distributed processing across multiple processing units is implemented, then scalability and processing capacity are improved, but system complexity and synchronization difficulty increase
Solution Approach 1:
The neural network is divided into multiple processing units, each responsible for specific compute tasks. This segmentation allows parallel processing and improves capacity while managing complexity through modular architecture where each unit handles discrete portions of the computational graph.
Solution Approach 2:
An interposer layer is introduced as an intermediary component between processing units, providing standardized communication interfaces and coordination mechanisms. This mediator simplifies the complexity of direct inter-unit communication by providing a unified protocol for data exchange and task coordination.
2Productivity
If data is transferred across processing units, then computation can be distributed, but latency and power consumption increase
Solution Approach 1:
Each processing unit is equipped with local high-bandwidth memory (HBM) stacks that provide fast, low-latency storage close to the compute units. This local quality allows data to be cached and processed locally rather than requiring frequent transfers across the interposer, reducing latency while maintaining distribution benefits.
3Speed
If high-bandwidth memory stacks are integrated with compute layers, then data access speed is improved, but manufacturing complexity increases
Solution Approach 1:
The HBM stacks are vertically integrated with compute layers in a nested configuration where memory and compute components are stacked one on top of another within the same processing unit. This nesting achieves high-speed data access through short distances while the modular stacked architecture actually simplifies manufacturing by using standardized vertical integration processes.
4Productivity
If compute tasks are partitioned across processing units, then processing capacity increases, but task management and synchronization difficulty increase
Solution Approach 1:
The interposer layer serves as a coordination mediator that manages task distribution and synchronization between processing units. It provides centralized task management interfaces and automated coordination protocols, making task management easier despite the distributed architecture.
Solution Approach 2:
Task partitioning and data distribution are performed in advance before execution begins. The system pre-partitions compute tasks and pre-loads required data to appropriate processing units, eliminating the need for complex runtime coordination and simplifying operational management during execution.
Data Source
AI summary
Machine learning model scalability with distributed multi-layer processing is disclosed herein. A method for processing and deploying machine learning models that enhances scalability and efficiency by executing a subset of a neural network on each of a plurality of interconnected processing units. The method involves partitioning compute tasks across these processing units to reduce latency, including broadcast and reduction processes for inputs and outputs. It also includes managing the allocation of samples in a batch to specific master processing units within the distributed arrangement and synchronizing computation between fully connected layers within each processing unit. Additionally, the method implements data reduction during the transfer of data across the processing units, wherein data is accumulated with a current processing unit's partial sum as it is transferred to the destination processing unit.