A Reconfigurable Framework for AI-FPGA Agent Integration and Acceleration

TL;DR

This paper introduces a reconfigurable AI-FPGA framework that simplifies model deployment, achieving significant latency and energy efficiency improvements for neural network inference in constrained environments.

Contribution

It proposes an agent-driven system that dynamically partitions models and manages data transfers, streamlining AI-FPGA integration and acceleration.

Findings

Over 10x latency reduction compared to CPU

2-3x higher energy efficiency than GPU

Maintains classification accuracy within 0.2% of full-precision

Abstract

Artificial intelligence (AI) is increasingly deployed in real-time and energy-constrained environments, driving demand for hardware platforms that can deliver high performance and power efficiency. While central processing units (CPUs) and graphics processing units (GPUs) have traditionally served as the primary inference engines, their general-purpose nature often leads to inefficiencies under strict latency or power budgets. Field-Programmable Gate Arrays (FPGAs) offer a promising alternative by enabling custom-tailored parallelism and hardware-level optimizations. However, mapping AI workloads to FPGAs remains challenging due to the complexity of hardware-software co-design and data orchestration. This paper presents AI FPGA Agent, an agent-driven framework that simplifies the integration and acceleration of deep neural network inference on FPGAs. The proposed system employs a runtime software agent that dynamically partitions AI models, schedules compute-intensive layers for hardware offload, and manages data transfers with minimal developer intervention. The hardware component includes a parameterizable accelerator core optimized for high-throughput inference using quantized arithmetic. Experimental results demonstrate that the AI FPGA Agent achieves over 10x latency reduction compared to CPU baselines and 2-3x higher energy efficiency than GPU implementations, all while preserving classification accuracy within 0.2% of full-precision references. These findings underscore the potential of AI-FPGA co-design for scalable, energy-efficient AI deployment.