TF2AIF: Accelerating AI Model Inference

• TF2AIF is an automated toolchain that accelerates AI inference by converting TensorFlow models into optimized, containerized services for heterogeneous hardware.
• It employs specialized converters and quantization techniques to tailor models for different backends such as CPUs, GPUs, and FPGAs.
• The framework integrates resource-aware scheduling and dynamic orchestration to achieve energy-efficient deployment in cloud-edge and 6G/B5G environments.

TF2AIF (TensorFlow 2 Accelerator Integration Framework) is an automated toolchain and deployment abstraction designed to facilitate the acceleration of AI model inference across a heterogeneous landscape of hardware platforms, including CPUs, GPUs, and FPGAs. By generating a family of optimized, containerized inference services for diverse targets from a single high-level TensorFlow function, TF2AIF enables researchers and practitioners to deploy AI workloads seamlessly throughout the cloud-edge continuum, removing the need for manual adaptation to each hardware backend. Its integration with orchestration frameworks further allows for resource-aware and dynamic service placement, a critical feature for energy-efficient operation in latency- and power-constrained 6G and B5G environments (Leftheriotis et al., 2024, Yunusoglu et al., 27 Jan 2026).

1. System Architecture and Pipeline Stages

The end-to-end TF2AIF pipeline consists of four primary stages:

1. User Input Preparation:
   • A TensorFlow2 model definition, typically in Python (e.g., a Keras .h5 or SavedModel), alongside an experiment server stub (~100 lines, for preprocessing and postprocessing), a calibration dataset accessor (for quantization), and a YAML configuration declaring targets, batch size, and image registry.
2. Model Conversion:
   • For each declared hardware backend, TF2AIF invokes the appropriate compiler: TF-Lite Converter for x86/ARM (FP32, INT8), TF-TRT or ONNX+TensorRT for NVIDIA GPUs (FP16/INT8), and Vitis AI for Xilinx FPGAs. Quantization, if enabled, employs calibration data.
3. Composer and Containerization:
   • The Composer merges a base Docker image (with correct drivers and libraries), a platform-specific inference runtime (C++/Python), and the user’s server stub into a container. A lightweight Flask or FastAPI app exposes two endpoints: /infer and /metrics.
4. Client and Metrics Generation:
   • TF2AIF creates a corresponding client container that queries /infer and /metrics, tracking per-request performance.

The result is a set of containerized AI functions, each matched to a specific hardware target. This design abstracts heterogeneity, presenting a uniform deployment interface throughout the cloud-edge continuum (Leftheriotis et al., 2024).

2. Toolchain Integration and Resource-Aware Scheduling

Upon invoking TF2AIF with a model directory and configuration file, all conversion and build tasks are launched in parallel, leveraging Python multiprocessing. The core sequence:

1. Model Conversion:
   • The selected converter (e.g., tf.lite.TFLiteConverter, TF-TRT API, Vitis AI quantizer) processes the input model for each backend.
2. Registry Push:
   • Converted artifacts are uploaded to an artifact registry.
3. Container Build and Push:
   • Each hardware-specific build context is created via Docker Buildx and published to an image repository.

For cluster-level deployment, TF2AIF is designed to interoperate with Kubernetes or comparable orchestrators. Integration exposes a /metrics endpoint reporting latency, throughput, and other KPIs. Resource-aware placement is formulated as a mixed-integer optimization problem: for a set of $M$ models and $N$ nodes, binary variable $x_{i,j}=1$ if model $i$ is assigned to node $j$, subject to CPU, GPU, and memory constraints per node and exclusive assignment per model (Leftheriotis et al., 2024). Objective functions may minimize sum-latency, energy, or a weighted combination.

3. Supported Backends and Abstraction Layer

TF2AIF supports the following hardware/software combinations out-of-the-box:

Target	Backend	Precision
x86 CPU	TensorFlow Lite	FP32
ARMv8 CPU	TensorFlow Lite	INT8
NVIDIA V100	ONNX+TensorRT	FP16/INT8
Jetson AGX	ONNX+TensorRT	INT8
Xilinx U280	Vitis AI	INT8

Abstraction mechanisms include a uniform Converter class with a .convert() interface for each backend, Docker base images providing the runtime environment, and user hooks for pre/postprocessing that are leveraged across all targets. Quantization, if used, is orchestrated identically for all backends, ensuring minimal need for backend-specific logic by the AI developer (Leftheriotis et al., 2024).

4. FPGA Acceleration via AI FPGA Agent and Model Partitioning

When targeting FPGAs, TF2AIF interoperates with frameworks such as AI FPGA Agent. The system architecture includes:

• Host CPU: Executes the TensorFlow application, preprocesses input, runs the AI-FPGA Agent software agent, and handles DMA and FPGA driver routines.
• Interconnect: Either PCIe Gen3 x8 for discrete cards or 64-bit AXI4 for SoC FPGAs.
• FPGA Device: Incorporates a DMA engine (with double descriptors for ping/pong buffering), on-chip storage (BRAM/URAM), and a parameterizable accelerator core.

Model Partitioning and Scheduling: The software agent dynamically assigns each neural network layer to CPU or FPGA by optimizing a multi-objective function:

$$\min_{x_1,\dots,x_N} F = \alpha \sum_{i=1}^N \bigl(x_i\,L_{\text{FPGA},i} + (1-x_i)\,L_{\text{CPU},i}\bigr) + \beta \sum_{i=1}^N \bigl(x_i\,E_{\text{FPGA},i} + (1-x_i)\,E_{\text{CPU},i}\bigr)$$

where $x_i$ indicates assignment, $L_\cdot$ denotes latency per layer, $E_\cdot$ is energy, and $\alpha,\,\beta$ control trade-offs. The scheduler can employ heuristic or Q-learning-based policies to choose assignments, incorporating real-time resource usage and supporting hardware constraints (e.g., forced fallback if FPGA exhaustion is detected) (Yunusoglu et al., 27 Jan 2026).

5. Accelerator Core Architecture and Data Orchestration

FPGAs are programmed with parameterizable accelerator cores, designed for high-throughput, quantized inference. Principal aspects:

• Arithmetic: Multiply-accumulate (MAC) arrays operate on quantized weights and activations ($N$0-bit), with $N$1 parallel lanes in SIMD.
• Dataflow: Pipelined stages cover weight fetch, tiling, multiplication/accumulation, activation (e.g., ReLU/SiLU), and output to external memory via DMA.
• Parameterization: Core can be tuned via bit-width, parallelism ($N$2), and tile sizes $N$3 for performance/resource trade-offs.

Throughput is quantified as:

$N$4

where $N$5 is frequency and $N$6 is degree of parallelism. Orchestration relies on double-buffered memory movement, with per-tile latency given by

$N$7

$N$8

This ensures overlap of transfer and compute, minimizing overall latency (Yunusoglu et al., 27 Jan 2026).

6. Workflow: From TensorFlow Model to Deployed Inference Service

The complete TF2AIF workflow for FPGA acceleration involves:

• Training and exporting a TensorFlow2 SavedModel.
• Performing post-training quantization, e.g., to 8-bit INT.
• Converting the quantized model to an intermediate format (e.g., .aif) and specifying tiling.
• Generating HLS kernel stubs and synthesizing the FPGA bitstream (e.g., using Vivado HLS).
• Packing the necessary artifacts into a containerized inference service with runtime, model, and server stub components.
• Deploying the service with a command-line interface that specifies bitstream and batch size, and then invoking inference via REST or direct call.

A standardized metrics interface allows for collection of per-image latency, throughput, and energy efficiency statistics, supporting downstream orchestration (Leftheriotis et al., 2024, Yunusoglu et al., 27 Jan 2026).

7. Experimental Evaluation and Observed Trade-offs

TF2AIF's experimental results confirm robust acceleration and deployment flexibility. For representative models such as ResNet50 and InceptionV4, speedups on an NVIDIA V100 reached 10.3× and 11.2×, respectively, over native CPUs, while AI FPGA Agent achieved over 10× latency reduction versus CPU and 2–3× energy efficiency over GPUs, with accuracy maintained within 0.2% of the reference (Yunusoglu et al., 27 Jan 2026, Leftheriotis et al., 2024).

Backend	Average Speedup
CPU	3.6×
ARM-INT8	2.7×
V100-FP16	7.6×
Jetson-INT8	5.5×

Trade-offs include container image sizes (~1GB for GPU), build times (30–60 seconds per variant), and occasional quantization-induced accuracy shifts, necessitating validation. Energy consumption must be estimated externally, as on-die power is not directly measured. Extending to new hardware requires only lightweight Python and Docker additions unless highly specialized accelerators are involved (Leftheriotis et al., 2024).

8. Extensibility and Future Directions

TF2AIF supports extensibility via user-defined quantization schemes (e.g., INT4/AWQ, mixed-precision per layer), new operator support (e.g., attention or RoPE for transformers) via additional HLS modules, and partial reconfiguration of FPGA bitstreams at runtime. Scheduling mechanisms can leverage performance and energy metrics for dynamic placement, further optimizing low-latency, energy-efficient service composition in evolving 6G/N6G scenarios (Yunusoglu et al., 27 Jan 2026).

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TF2AIF represents a unified, resource-agnostic deployment strategy for TensorFlow-based inference workloads, providing a basis for scalable, energy-aware orchestration of AI services across heterogeneous high-performance environments (Leftheriotis et al., 2024, Yunusoglu et al., 27 Jan 2026).