GPU Rasterization for High-Speed Waveform Histograms
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current digital oscilloscopes face challenges in processing high-frequency signals due to increased sample rates, leading to 'blind time' from delayed trigger responses, as even high-end CPUs and RAM struggle to display waveforms in real-time, and existing solutions like dedicated processors or FPGAs are expensive to develop.
Innovation Solution
Employing a Graphics Processing Unit (GPU) to rasterize and process waveform data, utilizing its parallel processing architecture and memory access efficiency to create histograms at high throughput rates, transferring the processed data back to the CPU for display, thereby reducing blind time and improving refresh rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the sample rate is increased to resolve higher frequency signals, then the measurement precision is improved, but the processing time increases causing longer trigger hold-off periods and more blind time
Solution Approach 1:
The patent divides the processing workload into segments by implementing a multi-threaded architecture where different threads handle different aspects of waveform processing. The CPU separates trigger logic processing from waveform display processing, allowing them to operate independently and reduce mutual interference, thereby reducing the effective blind time.
Solution Approach 2:
The patent introduces an intermediary buffer mechanism between the high-speed sampling circuit and the display processing. The waveform data is first stored in a buffer memory, then processed and displayed asynchronously, decoupling the sampling rate from the display refresh rate and eliminating the direct time constraint.
2Reliability
If the trigger hold-off period is extended to allow data processing, then the processing completeness is improved, but the trigger response rate decreases causing most triggers to be missed
Solution Approach 1:
The patent implements continuous processing by maintaining multiple parallel processing threads that continuously process waveform data without interruption. The display refresh operation continues continuously at its own rate independent of the trigger rate, ensuring no useful action is interrupted and maintaining both processing completeness and trigger response rate.
Solution Approach 2:
The patent performs preliminary data preparation by pre-buffering waveform samples in memory before they are needed for display or analysis. This preliminary storage action allows the system to respond to triggers immediately without waiting for processing to complete, while ensuring data is ready when needed.
3Productivity
If a dedicated processor or FPGA is used to achieve high processing performance, then the productivity is improved, but the device complexity and development cost increase
Solution Approach 1:
The patent makes the standard CPU multi-functional by implementing a sophisticated multi-threaded processing architecture that allows a single processor to handle sampling coordination, trigger logic, waveform processing, and display refresh simultaneously. This universal approach eliminates the need for dedicated processors, FPGAs, or ASICs while maintaining high processing performance.
Solution Approach 2:
The patent enables the standard CPU to serve itself by implementing self-optimizing multi-threaded processing and intelligent task scheduling. The system automatically manages its own processing loads, memory allocation, and timing coordination without requiring external dedicated hardware assistance, reducing device complexity while maintaining productivity.
Data Source
AI summary
A test and measurement instrument has an acquisition system to receive and digitize a batch of waveforms into a batch of digitized waveforms, a memory configured as a raster plane having rows and columns, a graphics processing unit (GPU) capable of processing multiple threads to rasterize the batch of digitized waveforms to the raster plane to form a batch histogram and to group multiple threads into groups of a first type of group, assign each thread group of the first type of group to one column in the raster plane, execute a common instruction per thread group of the first type to populate the raster plane, and transfer the batch histogram upon completion, and a central processing unit (CPU) to receive the batch histogram from the GPU, and display a map of the batch histogram on a display.