A Reconfigurable Framework for AI-FPGA Agent Integration and Acceleration

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.

💡 Research Summary

The paper introduces AI‑FPGA Agent, a novel framework that bridges high‑level deep‑learning model development with low‑level FPGA acceleration through a dynamic, agent‑driven runtime. Recognizing that CPUs and GPUs, while ubiquitous, often fall short in latency‑critical or power‑constrained edge scenarios, the authors propose a co‑design approach where a software agent running on the host CPU continuously partitions a neural‑network graph and decides which layers to offload to a custom FPGA accelerator.

Key technical contributions include:

1. Reinforcement‑learning based scheduling – The agent employs Q‑learning with an ε‑greedy policy. It observes the current system state (e.g., compute intensity, memory pressure, power budget), receives a reward signal reflecting latency and energy outcomes, updates a primary Q‑table (Q_A), and periodically synchronizes with a target Q‑table (Q_B) to stabilize learning. This enables the system to adapt to runtime variations rather than relying on static, design‑time mapping.

2. Parameterizable accelerator core – The FPGA side is a modular accelerator whose key parameters (bit‑width, pipeline depth, number of MAC units, on‑chip buffer sizes) can be tuned at synthesis time. The core implements quantized arithmetic (e.g., 8‑bit, 4‑bit AWQ) and a high‑throughput data path, allowing large models such as LLaMA‑2‑7B to be stored largely in DDR4 while still achieving high bandwidth utilization (≈85 %).

3. End‑to‑end tool flow – The authors integrate a SystemC‑based functional and timing simulation stack, a hardware driver, and an HLS/RTL synthesis step that produces the final bitstream. This flow guarantees that functional correctness is verified before hardware deployment, reducing iteration cycles.

Experimental evaluation was performed on a Xilinx KV260 embedded platform. The 4‑bit quantized LLaMA‑2‑7B model occupies ~93 % of the 4 GB DDR4, with a KV cache of 264 MB. The agent dynamically decides which layers to execute on the FPGA versus the CPU. Compared with a CPU‑only baseline, the framework achieves an average 10.3× latency reduction. Against a GPU implementation, it delivers 2.4–2.9× better energy efficiency while maintaining classification accuracy within 0.2 % of the full‑precision reference.

Strengths:

• The use of reinforcement learning for runtime scheduling is innovative and addresses the static‑mapping limitation of many prior FPGA AI frameworks.
• The accelerator’s configurability makes the approach applicable to a range of models and quantization schemes.
• The high‑level API abstracts away HDL details, lowering the barrier for AI developers.

Weaknesses / Open Issues:

• The paper does not quantify the overhead introduced by the Q‑learning agent (learning time, exploration cost) and how it scales with larger workloads.
• Evaluation is limited to a single FPGA board and a single model family; broader benchmarks (CNNs, transformers, multi‑task workloads) and multi‑FPGA or data‑center‑scale scenarios are missing.
• The design is tightly coupled to Xilinx tools and the KV260 platform, raising questions about portability to Intel or other FPGA ecosystems.
• Resource utilization (LUT, BRAM, DSP) and detailed power measurement methodology are not fully disclosed, which hampers reproducibility.
• Comparison against other dynamic runtime frameworks (e.g., Vitis AI Runtime, OpenCL‑based schedulers) is absent, making it difficult to assess relative merit.

Conclusion and future directions: AI‑FPGA Agent demonstrates that a reinforcement‑learning‑guided scheduler combined with a flexible quantized accelerator can deliver substantial latency and energy gains for edge AI inference. To transition from prototype to production‑grade solution, future work should explore (i) scaling the approach to larger, multi‑FPGA systems, (ii) automating the selection of accelerator parameters via meta‑learning, (iii) extending support to additional FPGA vendors, and (iv) providing a more exhaustive performance and power analysis across diverse AI workloads.