Accelerator Debug Circuitry for Breakpoint-Based Core Analysis
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Solution Overview
Problem
Debugging hardware accelerators, particularly those used for computationally-intensive tasks like AI/ML and image processing, is a time-consuming and complex task due to the lack of dedicated hardware support and the difficulty in identifying errors in large datasets and parallel core execution.
Innovation Solution
Implementing debug circuitry that allows for halting execution at specified breakpoints, single-stepping through output transactions, and analyzing data at specific points during AI/ML model execution, thereby isolating performance bottlenecks and reducing the time and effort required to identify bugs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If hardware accelerators are designed for high-performance computationally-intensive processing, then processing capability is improved, but debugging complexity increases
Solution Approach 1:
The hardware accelerator is segmented into multiple cores that can be independently controlled and debugged. Each core has its own register set and execution state, allowing individual debugging of specific cores without affecting the entire accelerator. This segmentation reduces overall debugging complexity by enabling targeted analysis of problematic cores.
Solution Approach 2:
A debug controller is introduced as an intermediary component between the hardware accelerator cores and the software debugger. This debug controller manages the complex interaction between multiple cores, handles breakpoint conditions, and coordinates single-stepping operations. By centralizing debugging control in this intermediary device, the complexity of debugging parallel hardware accelerators is significantly reduced.
2Productivity
If parallel core execution is implemented to improve processing speed, then productivity is improved, but error identification difficulty increases
Solution Approach 1:
The debug controller continuously monitors the execution state of all parallel cores and provides feedback to the software debugger. When a breakpoint condition is met in any core, the system automatically halts execution and reports the specific core and breakpoint location. This feedback mechanism enables easy identification of errors in parallel execution without manual analysis of all core states.
Solution Approach 2:
Breakpoints and debugging conditions are set in advance before execution begins. The debug controller is pre-configured with breakpoint addresses and conditions for each core. During parallel execution, when these pre-set conditions are met, the system automatically responds with halted execution and detailed state information, eliminating the need for post-error analysis.
3Ease of operation
If dedicated hardware debug support is added to hardware accelerators, then debugging ease is improved, but device complexity increases
Solution Approach 1:
The debug controller serves multiple functions: it manages breakpoints for all cores, handles single-stepping operations, monitors execution states, and interfaces with software debuggers. By consolidating these multiple debugging functions into a single multi-functional controller, the hardware complexity increase is minimized while maintaining comprehensive debugging capabilities.
Solution Approach 2:
The hardware accelerator includes self-service debugging capabilities where the debug controller automatically manages its own state, monitors core execution, and communicates debugging information without requiring external intervention. The system can autonomously halt execution at breakpoints, report core states, and manage debugging sessions, reducing the burden on external debugging tools and operators.
Data Source
AI summary
Methods, apparatus, systems, and articles of manufacture are disclosed to debug a hardware accelerator such as a neural network accelerator for executing Artificial Intelligence computational workloads. An example apparatus includes a core with a core input and a core output to execute executable code based on a machine-learning model to generate a data output based on a data input, and debug circuitry coupled to the core. The debug circuitry is configured to detect a breakpoint associated with the machine-learning model, compile executable code based on at least one of the machine-learning model or the breakpoint. In response to the triggering of the breakpoint, the debug circuitry is to stop the execution of the executable code and output data such as the data input, data output and the breakpoint for debugging the hardware accelerator.