In-Memory Compute Data Placement for HBM Timing Overheads
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Solution Overview
Problem
Current High Bandwidth Memory (HBM) architectures for graphics processing units (GPUs) lack efficient data placement strategies for in-memory compute operations, leading to suboptimal performance due to high DRAM timing overheads.
Innovation Solution
The implementation of a memory module with integrated in-memory compute (IMC) capabilities, where a memory controller determines optimal data layouts (such as 1OP, SR, and DR) to supply operands to DRAM banks, enabling efficient ALU operations within the IMC module.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If in-memory compute operations are implemented in HBM architecture, then computational performance is improved, but DRAM timing overheads increase
Solution Approach 1:
The memory controller pre-determines optimal data layouts (1OP, SR, DR) and pre-positions operands in DRAM banks before ALU operations are needed. This preliminary data placement preparation eliminates timing overheads by ensuring data is ready in the correct locations when computations start, rather than attempting to optimize during the computation process.
Solution Approach 2:
The system segments data into different layouts (1OP, SR, DR) based on the specific computational requirements. By dividing the data management process into distinct layout strategies and using multiple DRAM banks independently, the system can optimize for different computational patterns without affecting other operations, thereby reducing overall timing overheads.
2Productivity
If multiple data layouts are supported for in-memory compute, then computational efficiency is improved, but device complexity increases
Solution Approach 1:
The memory controller is designed with multi-functionality to handle multiple data layouts (1OP, SR, DR) using the same hardware resources. This universal approach allows a single controller to optimize for different computational patterns without requiring separate dedicated hardware for each layout type, thereby managing complexity while maintaining high computational efficiency.
Solution Approach 2:
The system changes parameters such as data placement locations, access patterns, and buffer configurations based on the required computational operation. By dynamically adjusting these parameters rather than redesigning the hardware for each operation type, the system achieves high efficiency for multiple layouts while keeping the physical device structure relatively simple.
3Device complexity
If operands are supplied directly to IMC modules without optimized data placement, then device simplicity is maintained, but computational performance deteriorates
Solution Approach 1:
The memory controller performs preliminary data placement actions by determining optimal layouts and positioning operands in DRAM banks before they are needed for computation. This simple yet effective preliminary organization of data eliminates the need for complex real-time data management during computation, achieving high performance without complicating the overall system architecture.
Data Source
AI summary
According to one embodiment, a memory module includes: a memory die including a dynamic random access memory (DRAM) banks, each including: an array of DRAM cells arranged in pages; a row buffer to store values of one of the pages; an input/output (IO) module; and an in-memory compute (IMC) module including: an arithmetic logic unit (ALU) to receive operands from the row buffer or the IO module and to compute an output based on the operands and one of a plurality of ALU operations; and a result register to store the output of the ALU; and a controller to: receive, from a host processor, operands and an instruction; determine, based on the instruction, a data layout; supply the operands to the DRAM banks in accordance with the data layout; and control an IMC module to perform one of the ALU operations on the operands in accordance with the instruction.SPL