CXL Tensor Memory Access via Hierarchical Cache Mapping
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Solution Overview
Problem
Current semiconductor devices face challenges in achieving high-speed memory access and reduced power consumption, particularly in systems with multi-core processors that require efficient access patterns for storage devices, which is crucial for tensor processing in machine learning and artificial intelligence applications.
Innovation Solution
The implementation of Compute Express Link (CXL) technology, which enables cache-coherent interconnects for processors, memory expansion, and accelerators, allowing for efficient tensor memory access operations by using CXL units with tensor access circuits, memory mappers, and address translators to optimize memory access patterns, particularly in Tier 1, 2, and 3 CXL systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If conventional memory access patterns are used in multi-core processor systems, then basic memory operations can be performed, but memory access speed and power consumption are insufficient for high-performance tensor processing
Solution Approach 1:
The system performs preliminary actions by pre-loading frequently accessed tensor data into cache memory before actual computation begins. The memory controller anticipates access patterns and prepares data in advance, reducing the need for high-speed sequential access during computation and thereby lowering overall power consumption while maintaining high performance
Solution Approach 2:
The memory access system is segmented into multiple hierarchical levels (L1 cache, L2 cache, L3 cache, and main memory) with different access speeds and capacities. Each level handles specific access patterns, allowing the system to optimize between speed and power consumption by utilizing appropriate cache levels for different data access scenarios in tensor processing
2Productivity
If repeated memory access is performed for tensor processing operations, then computational efficiency can be improved, but access time and power consumption increase
Solution Approach 1:
The memory controller implements feedback mechanisms that monitor access patterns and adjust memory access strategies in real-time. By analyzing access sequences and predicting future access needs, the system optimizes repeated accesses for tensor operations, reducing total access time while maintaining high computational efficiency through adaptive memory management
Solution Approach 2:
Multiple memory access operations are merged into batched transactions when possible. The system combines sequential access patterns into unified memory operations, reducing the overhead of individual access commands and minimizing total access time while maintaining the computational efficiency required for tensor processing
3Adaptability or versatility
If standard memory access patterns are used, then compatibility with existing systems is maintained, but performance for tensor operations is insufficient
Solution Approach 1:
The memory controller is designed with multi-functionality that supports both conventional memory access patterns for compatibility and specialized tensor access patterns for enhanced performance. The system can dynamically switch between standard and optimized access modes, maintaining compatibility with existing software while achieving superior tensor processing performance through hardware-level optimizations
Data Source
AI summary
An example of compute express link (CXL) system includes a memory, and a tensor access circuit having a memory mapper configured to configure a memory map based on a compute express link (CXL) command associated with an access operation of the memory. The memory map includes a specific sequence of CXL instructions to access to the memory via a CXL bus.