Pipelined FPGA Sensor Data Processing for Autonomous Vehicle Latency
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Solution Overview
Problem
Current systems for autonomous vehicles face latency issues in processing sensor data, which hinders real-time object detection and classification, essential for accurate motion planning and navigation.
Innovation Solution
Implementing a field programmable gate array (FPGA) device that processes sensor data in a pipelined manner, pre-processing increments of data as they become available and feeding them into machine-learned models for immediate inference, reducing latency and enhancing processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If sensor data is processed using conventional systems, then processing can be performed, but processing latency is high which hinders real-time object detection and classification
Solution Approach 1:
The sensor data processing is divided into multiple pipeline stages including pre-processing, feature extraction, and machine-learned model inference. Each stage processes data independently and concurrently, allowing parallel execution of multiple operations on different data increments simultaneously, thereby reducing overall processing latency
Solution Approach 2:
The FPGA device begins pre-processing sensor data increments as soon as they become available from the sensor, before the complete image frame is captured. This preliminary action allows subsequent processing stages to work on already-preprocessed data, overlapping the pre-processing time with data acquisition time and reducing total latency
2Productivity
If sensor data is processed incrementally as it becomes available, then processing efficiency is improved, but system complexity increases due to pipelined architecture requirements
Solution Approach 1:
The FPGA device is configured to perform multiple functions within the same hardware architecture: pre-processing operations, feature extraction, and machine-learned model inference. This multi-functionality allows incremental processing without requiring separate dedicated hardware for each processing stage, reducing overall system complexity
Solution Approach 2:
The processing pipeline is structured with nested stages where pre-processing operations are embedded within the data flow that feeds into feature extraction, which in turn feeds into the machine-learned model. This nesting allows data to flow continuously through multiple processing levels without requiring complex inter-stage coordination, simplifying the overall architecture
Data Source
AI summary
In one example embodiment, a computer-implemented method includes obtaining sensor data from a sensor, the sensor data corresponding to an image frame, and the sensor data including a first portion that corresponds to a portion of the image frame. The method includes pipelining the first portion of the sensor data into a machine-learned model before the sensor data corresponding to the entire image frame is transferred from the sensor to a memory device, to perform one or more inference operations on the first portion of the sensor data. The method includes generating as an output of the machine-learned model, in response to pipelining the sensor data corresponding to each portion of the image frame into the machine-learned model, a detection or classification of the one or more objects indicated within the sensor data.