A Resource-Driven Approach for Implementing CNNs on FPGAs Using Adaptive IPs

2025-10-03

AI Summary

Deploying CNNs on FPGAs faces a fundamental trade-off between stringent resource constraints and low-latency requirements. Method: This paper proposes a resource-driven, adaptive convolution IP core design methodology. It constructs a parameterized, configurable convolution IP library enabling joint optimization of DSP usage, logic resources, and fixed-point precision—ensuring cross-architecture portability and scalability. Four adaptive IP cores, tailored to distinct resource budgets, are implemented in VHDL with fixed-point arithmetic and augmented with a resource-aware dynamic configuration mechanism. Results: Evaluated on the Xilinx Zynq UltraScale+ platform, the approach maintains real-time inference while significantly improving resource utilization and deployment flexibility. Compared to state-of-the-art FPGA-based CNN accelerators, it achieves superior performance–resource efficiency and broader applicability across diverse CNN models and FPGA devices.

Technology Category

• Machine Learning: Hardware-aware ML
• Computer Vision: Learning & Optimization for CV
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

The increasing demand for real-time, low-latency artificial intelligence applications has propelled the use of Field-Programmable Gate Arrays (FPGAs) for Convolutional Neural Network (CNN) implementations. FPGAs offer reconfigurability, energy efficiency, and performance advantages over GPUs, making them suitable for edge devices and embedded systems. This work presents a novel library of resource-efficient convolution IPs designed to automatically adapt to the available FPGA resources. Developed in VHDL, these IPs are parameterizable and utilize fixed-point arithmetic for optimal performance. Four IPs are introduced, each tailored to specific resource constraints, offering flexibility in DSP usage, logic consumption, and precision. Experimental results on a Zynq UltraScale+ FPGA highlight the trade-offs between performance and resource usage. The comparison with recent FPGA-based CNN acceleration techniques emphasizes the versatility and independence of this approach from specific FPGA architectures or technological advancements. Future work will expand the library to include pooling and activation functions, enabling broader applicability and integration into CNN frameworks.

Problem

• Optimizing CNN implementation on FPGAs with resource-adaptive IP cores
• Balancing performance and resource usage for edge AI applications
• Creating architecture-independent CNN acceleration for various FPGA constraints

Innovation

• Resource-efficient convolution IPs adapt to FPGA resources
• Parameterizable VHDL IPs use fixed-point arithmetic
• Four IPs tailored to specific resource constraints