CGR Compute Unit Context Switching for Flexible Dataflow Execution
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Solution Overview
Problem
Existing dataflow computing systems lack flexibility in configuring and switching between tasks and operations in coarse-grained reconfigurable processors, leading to inefficiencies in executing complex dataflow graphs, particularly in machine learning and artificial intelligence workloads.
Innovation Solution
A CGR processor with an array of PCUs and PMUs, where PCUs have SIMD units forming a datapath and PMUs have a multi-segment datapath pipeline, allowing dynamic switching between tasks and operations using configuration files, and finite state machines manage context transitions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a general-purpose processor is used to execute computer programs, then versatility is maintained, but execution efficiency and energy consumption are insufficient for specialized workloads
Solution Approach 1:
The processor employs dynamic reconfiguration capabilities where the datapath and compute units can be programmatically adjusted during operation. Configuration files enable the system to transition between different operational modes (e.g., SIMD mode, reduction mode, broadcast mode) to optimize performance for specific workload types while maintaining versatility through software-controlled adaptability.
Solution Approach 2:
The system changes operational parameters through configuration files that modify the behavior of compute units and datapath elements. By altering parameters such as datapath width, compute unit configuration, and operational mode, the processor achieves high efficiency for specialized tasks while retaining the ability to adapt to different computational requirements.
2Productivity
If the datapath is configured for a specific operation, then operational efficiency is improved, but flexibility to switch between different operations is reduced
Solution Approach 1:
The datapath is designed as a universal structure that can perform multiple operations through configuration. The same physical datapath elements (ALUs, multiplexers, registers) can be reconfigured via configuration files to execute different operations (arithmetic, logic, data movement), achieving both efficiency for current operations and flexibility for future operations.
Solution Approach 2:
The datapath configuration is made dynamic through the use of configuration files that can be loaded at runtime. This allows the system to switch between different operational configurations efficiently, maintaining high performance for the current task while enabling rapid adaptation to new operations when needed.
3Adaptability or versatility
If multiple PCU contexts are maintained for different tasks, then task switching capability is improved, but resource overhead and complexity increase
Solution Approach 1:
Instead of maintaining fully separate hardware contexts for each task, the system uses configuration files that represent contextual information. These configuration files act as lightweight copies or descriptions of the desired PCU state, enabling task switching without duplicating complex hardware structures. The configuration files store the necessary state information (datapath configuration, compute unit settings) that can be rapidly loaded and applied.
Data Source
AI summary
A non-transitory computer readable medium having instructions encoded thereon for configuring solutions for reconfigurable dataflow computing systems comprises a coarse-grained reconfigurable (CGR) processor and a compiler configured to generate one or more configuration files for an application for execution on the CGR processor. The CGR processor includes an array of pattern compute units (PCUs) and pattern memory units (PMUs). A PCU comprises a plurality of single-instruction multiple data (SIMD) units configurable to form a datapath. The CGR processor is coupled to configure a datapath including a SIMD, using a set of configurations bits corresponding to an operation related to the task. The CGR processor is coupled to switch among the plurality of tasks and their corresponding PCU contexts during execution of the dataflow graph.