Technology Category
Machine Learning: Hardware-aware MLCognitive Modeling & Cognitive Systems: Agent ArchitecturesMultiagent Systems: Agent/AI Theories and Architectures
A Reconfigurable Framework for AI-FPGA Agent Integration and Acceleration
🤖 AI Summary
This work addresses the challenges of efficiently deploying AI models under real-time and energy-constrained conditions, where conventional CPU/GPU platforms fall short and FPGA-based solutions remain hindered by complex hardware-software co-design and data scheduling. To overcome these limitations, the authors propose the AI FPGA Agent framework, which introduces a novel runtime software agent mechanism to enable dynamic model partitioning, hardware task scheduling for compute-intensive layers, and automated data transfer management. Integrated with a configurable quantized arithmetic acceleration core, this approach yields a low-intervention, energy-efficient, and reconfigurable FPGA inference system. Experimental results demonstrate over 10× latency reduction compared to CPU baselines and 2–3× higher energy efficiency than GPUs, while keeping classification accuracy degradation within 0.2%.
Technology Category
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
AI-FPGA integration
hardware-software co-design
deep neural network inference
energy-efficient AI
FPGA acceleration
Innovation
Methods, ideas, or system contributions that make the work stand out.
FPGA acceleration
AI agent
hardware-software co-design
quantized inference
reconfigurable framework
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