FPGA based Machine Learning Inference

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Machine learning (ML) is a branch of artificial intelligence that enables computers to learn from data and make predictions or decisions. ML applications are becoming more widespread and diverse, ranging from image recognition, natural language processing, recommender systems, to self-driving cars. However, ML models are often computationally intensive and require high-performance hardware to run efficiently.

One of the challenges of ML is to deploy the trained models on the target devices, such as edge devices, embedded systems, or cloud servers. This process is called ML inference, and it involves executing the model on new input data and producing the output. ML inference can have different requirements depending on the application domain, such as latency, accuracy, power consumption, or scalability.

FPGA has several advantages for ML inference, such as:

• High performance: FPGA can exploit parallelism and pipelining to achieve high throughput and low latency. FPGA can also support custom data types and operations that are optimized for ML models.
• Low power: FPGA can operate at low voltage and frequency, and can dynamically adjust the power consumption according to the workload. FPGA can also reduce the memory access and data movement by implementing on-chip memory and communication.
• Flexibility: FPGA can be reprogrammed to adapt to different ML models and algorithms, as well as different hardware platforms and interfaces. FPGA can also support hardware/software co-design and co-optimization.

In this blog post, we will introduce some of the key concepts and techniques for FPGA based ML inference, such as:

- How to design and implement an FPGA accelerator for a specific ML model or task

- How to use high-level synthesis (HLS) tools to generate FPGA code from C/C++ or Python

- How to integrate the FPGA accelerator with the software stack and the host system

- How to evaluate and benchmark the FPGA accelerator in terms of performance, power, and accuracy

- How to optimize the FPGA accelerator for different objectives and constraints

We hope that this blog post will provide you with a comprehensive overview of FPGA based ML inference, and inspire you to explore this exciting and promising field.

To be updated ..