FPGA-Accelerated Object Detection with Deep Learning Techniques

Field-programmable gate arrays (FPGAs) have emerged as a vital class of reconfigurable hardware accelerators, offering a compelling balance of performance, power efficiency and flexibility for deep-learning-based object detection tasks. By implementing convolutional neural network (CNN) operations directly in programmable logic, FPGAs enable massive parallelism in convolution, pooling and activation stages, substantially reducing inference latency compared with general-purpose processors. Advances in model optimisation—such as pruning, quantisation and dynamic partial reconfiguration—have enhanced resource utilisation, permitting deployment of sophisticated detection networks on energy-constrained edge devices. Applications span autonomous vehicles, unmanned aerial vehicles, robotics, smart surveillance and precision agriculture, where on-device inference supports low-latency decision-making and mitigates reliance on cloud connectivity. Despite these benefits, FPGA acceleration presents challenges in design complexity, limited on-chip memory and fixed-point arithmetic trade-offs. Current efforts focus on automated design flows, high-level synthesis frameworks and hardware-software co-design strategies to streamline development and sustain throughput gains across evolving CNN architectures.

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One study developed a dynamic reconfigurable CNN accelerator on a Xilinx KV260 platform, optimising layer-configuration sequences and employing templated hardware regions to support YOLOv2-Tiny inference. The design achieved over 75 GOPS at 250 MHz with a power efficiency exceeding 13 GOPS/W, demonstrating real-time detection capabilities on a sub-6 W edge device. Another work implemented a multi-object vehicle detection and tracking pipeline on FPGA, integrating structured pruning, 16-bit fixed-point quantisation and memory-interlayer multiplexing. This accelerator processed YOLOv3 and a Deepsort feature extractor in parallel, attaining up to 168 fps with significant model-size reductions and low power consumption. A benchmarking investigation further compared FPGAs against GPUs and TPUs for agricultural object detection, revealing that FPGA platforms delivered two to five times the frame rate of embedded GPUs while maintaining comparable accuracy and superior energy efficiency. Together, these contributions highlight practical methods for hardware optimisation, model compression and platform-specific tuning to meet the stringent requirements of edge intelligence.

FPGA-Accelerated Object Detection with Deep Learning Techniques publication trend

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Technical terms

Field-Programmable Gate Array (FPGA): A reconfigurable integrated circuit that can be programmed post-manufacture to implement custom digital logic.

Convolutional Neural Network (CNN): A class of deep neural network architectures designed for processing visual data via convolutional filters.

Pruning: The systematic removal of redundant or low-importance network parameters to reduce model complexity and computation.

Quantisation: The conversion of network weights and activations to lower-precision representations to improve arithmetic efficiency.

Dynamic Reconfiguration: The capability to modify sections of FPGA logic at runtime, enabling different network modules to share hardware resources.

Throughput: The volume of data a system can process per unit time, often expressed in frames per second (fps) or giga-operations per second (GOPS).

Latency: The elapsed time between input submission and output generation, critical for real-time responsiveness.

References

1. Benchmarking edge computing devices for grape bunches and trunks detection using accelerated object detection single shot multibox deep learning models. Engineering Applications of Artificial Intelligence (2023).
2. Efficient Dynamic Reconfigurable CNN Accelerator for Edge Intelligence Computing on FPGA. Information (2023).
3. FPGA-Based Vehicle Detection and Tracking Accelerator. Sensors (2023).