FPGA-Based Acceleration of Deep Learning Networks

Field programmable gate arrays (FPGAs) have emerged as flexible hardware platforms for accelerating deep learning networks, offering high energy efficiency, low latency and reconfigurable parallelism. By mapping neural network operations directly onto configurable logic blocks, embedded memory and specialised digital signal processing units, FPGAs exploit fine-grained pipeline architectures and customised dataflows to accelerate both training and inference. Unlike fixed-function ASICs or general-purpose GPUs, FPGAs permit dynamic adaptation to evolving network topologies, multi-precision arithmetic and on-the-fly reconfiguration, making them particularly well suited to embedded and edge applications. Key innovations include the deployment of intellectual property cores such as deep processing units for standardised convolutional neural network acceleration, and the application of roofline models to optimise resource utilisation against memory bandwidth. Hardware–software co-design approaches integrate ARM-based host processors with FPGA fabric to balance task scheduling, control logic and high-throughput compute kernels. Recent advances in model compression—through pruning, quantisation and parameter sharing—together with bespoke architectures for depthwise separable and dilated convolutions, have delivered multi-order improvements in throughput and energy efficiency. Global application areas span Internet of Things healthcare devices, real-time computer vision in autonomous vehicles, and satellite-borne Earth observation, where in-situ processing alleviates bandwidth constraints and enhances system autonomy. These FPGA-based solutions bridge the gap between performance, power and flexibility, underpinning the next generation of accessible, low-power deep learning deployments.

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FPGA-Based Acceleration of Deep Learning Networks publication trend

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Technical terms

Field programmable gate array (FPGA): A reconfigurable integrated circuit that can be programmed to implement custom logic and memory architectures, enabling tailored hardware acceleration.

Convolutional neural network (CNN): A class of deep learning models that apply convolutional filters to input data, widely used for image and signal processing tasks.

Quantisation: The process of reducing the precision of network weights and activations, typically from floating-point to fixed-point or integer representations, to decrease memory footprint and computational cost.

Pruning: A compression technique that removes redundant or unimportant network parameters, reducing model size and hardware resource requirements without significantly impacting accuracy.

Depthwise separable convolution: A factorised form of convolution that splits standard convolution into depthwise and pointwise operations, greatly reducing computational complexity and resource utilisation.

References

1. An Optimised CNN Hardware Accelerator Applicable to IoT End Nodes for Disruptive Healthcare. IoT (2024).
2. An FPGA-Based CNN Accelerator Integrating Depthwise Separable Convolution. Electronics (2019).
3. Accelerating Neural Network Inference on FPGA-Based Platforms—A Survey. Electronics (2021).