ResNet AI Acceleration for Edge AI Inference
Artificial Intelligence is increasingly being used for applications such as image classification, industrial inspection, surveillance, and intelligent automation. However, sending every input to a remote cloud for processing can introduce latency, increase network bandwidth requirements, and raise data privacy concerns.
Edge AI addresses this challenge by moving AI inference closer to where the data is generated.
Our iW-Fibre PCIe Acceleration provides a flexible hardware platform for running AI workloads locally, enabling highly parallel processing with low and predictable latency.
ResNet AI Acceleration Deep Learning Workload for FPGA
- ResNet (Residual Network) is a widely used convolutional neural network architecture for image related AI tasks. Its key innovation is the introduction of residual connections, which helps very deep networks learn effectively.
- Instead of requiring every layer to learn a completely new representation, a residual block allows the network to preserve and reuse information:
This structure makes ResNet a useful representative workload for hardware acceleration because convolution operations involve a large amount of computation that can benefit from parallel execution.
ResNet AI Acceleration Inference at the Edge with an FPGA
The main advantage of FPGA acceleration is not simply raw processing speed. It is the ability to customize hardware around the workload.
- Parallel Processing: Multiple operations can be executed simultaneously, making FPGAs well suited to the highly parallel nature of neural network computations.
- Low and Predictable Latency: Processing can be performed locally without continuously sending data to a remote server. This is valuable for applications requiring rapid responses.
- Hardware Flexibility: Unlike a fixed function accelerator, an FPGA can be reconfigured for different AI models and processing requirements.
ResNet AI Acceleration Architecture for ResNet Inference
The architecture below shows how a deep learning model is prepared and deployed on the G43-AFibre-400G for FPGA based AI inference.
iW-Fibre PCI-Based Acceleration Card Resnet Architecture
The model moves through the software tools before reaching the FPGA AI Suite hardware, where the actual AI processing takes place.
- Model Preparation: The ResNET 50 TF Pretrained model is developed using frameworks such as TensorFlow and converted using OpenVINO.
- FPGA Optimization: The FPGA AI Suite Compiler prepares and optimizes the model for FPGA execution and generates the required configuration.
- FPGA Execution: The configuration is deployed to the FPGA AI Suite IP within the G43-AFibre-400G, where the model is processed using the FPGA’s compute and memory resources.
AI Inference Workflow Using an FPGA AI Accelerator
This workflow demonstrates how an image is processed using FPGA-accelerated ResNet inference.
The workflow begins with a user provided image, which is pre-processed on the host before being transferred to the FPGA. The FPGA AI Suite IP then runs the ResNet model to perform inference, and the resulting scores are sent back to the host as class probabilities. The class with the highest probability is reported as the final
Flow
- User Image – Image is provided to the host application.
- Image Pre-processing – The image is resized and normalized into the format expected by ResNet.
- FPGA Inference – The prepared image is sent to the FPGA, where the FPGA AI Suite IP runs the ResNet neural network.
- Class Probabilities – The inference results are returned to the host as probabilities for each possible class.
- Final Prediction – The class with the highest probability is selected as the prediction.
Edge AI Applications Using FPGA AI Acceleration
- Industrial Defect Inspection – Cameras capture products on the production line, and ResNet 50 running at the edge identifies defects locally and in real time, enabling immediate quality control decisions.
- Smart Surveillance – Edge cameras use ResNet 50 to analyze video frames locally for object or event detection, reducing the need to continuously send video to the cloud.
- Medical Image Analysis – Medical imaging systems can run ResNet 50 based classification locally to analyze images close to where they are captured, reducing data transfer and supporting faster analysis.
ResNet on an iW-Fibre PCIe Acceleration card, Fits low latency, parallel processing, flexibility, and efficient edge inference.
be PCIe accelerator demonstrates how Edge AI can move compute intensive inference closer to the data. By combining a proven deep learning model with programmable FPGA acceleration, the solution targe
For platform evaluation or additional information, contact mktg@iwave-global.com
