CGRA Nested-Loop Execution with Configurable Initiation Intervals
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Solution Overview
Problem
Conventional computer architectures face performance and energy constraints due to significant time and energy consumption in data movement between processors and memory, limiting compute efficiency and throughput.
Innovation Solution
Implementing a compute-near-memory (CNM) system with hybrid threading processors and a custom compute fabric, utilizing chiplet-based architectures and configurable initiation intervals for processing elements (PEs) to optimize execution of nested loops, enabling efficient execution of nested loop operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional Von Neumann architecture is used, then system simplicity is maintained, but data movement between processors and memory consumes significant time and energy, limiting compute efficiency
Solution Approach 1:
The system segments the compute fabric into multiple processing elements (PEs) organized in a grid topology, where each PE can independently execute computations. This segmentation allows parallel processing of nested loops across multiple PEs, reducing the time required for compute-intensive operations and mitigating the impact of data movement delays.
Solution Approach 2:
The patent introduces a two-dimensional grid topology of processing elements, transitioning from conventional linear or hierarchical processor arrangements. This dimensional change enables simultaneous execution of loop iterations across multiple spatial locations, improving throughput for nested loop computations and reducing effective data movement time through closer proximity of compute units to data.
2Productivity
If configurable initiation intervals are used for nested loops, then execution frequency and throughput are improved, but device complexity increases
Solution Approach 1:
The system implements dynamic control of processing element initiation intervals, allowing the initiation interval to be configured based on the specific nested loop structure and data dependencies. This dynamic adjustment optimizes the execution frequency of inner loops relative to outer loops, improving throughput for compute-intensive workloads while managing complexity through software-based configuration rather than hardwired fixed intervals.
Solution Approach 2:
The patent changes the initiation interval parameter as a configurable system variable that can be adjusted according to workload characteristics. By modifying this temporal parameter rather than adding complex hardware control logic, the system achieves optimized execution frequency for nested loops while keeping the physical device structure relatively simple.
3Productivity
If compute-near-memory architecture is implemented, then compute efficiency is improved, but system architecture complexity increases
Solution Approach 1:
The patent merges compute functionality with memory by implementing processing elements that can execute computations near data storage locations. This combining of compute and memory functions reduces the distance data must travel and enables compute-intensive operations to be performed where data resides, improving compute efficiency while distributing complexity across multiple simple, identical PE units rather than requiring a single complex centralized architecture.
Data Source
AI summary
Disclosed in some examples, are methods, systems, devices, and machine-readable mediums which provide for more efficient CGRA execution by assigning different initiation intervals to different PEs executing a same code base. The initiation intervals may be a multiple of each other and the PE with the lowest initiation interval may be used to execute instructions of the code that is to be executed at a greater frequency than other instructions than other instructions that may be assigned to PEs with higher initiation intervals.