Artificial Intelligence Projects on FPGA
Field-programmable gate arrays (FPGAs) have been around for a few decades now. These devices have an array of logic blocks and a way to program the blocks and the relationships among them.
An FPGA is a generic tool that can be customized for multiple uses. In contrast to classic chips, FPGAs can be reconfigured multiple times for different purposes. To specify the configuration of an FPGA, developers use hardware description languages (HDLs) such as Verilog and VHDL.
FPGAs can be programmed for different kinds of workloads, from signal processing to deep learning and big data analytics. In this article, we focus on the use of FPGAs for Artificial Intelligence (AI) workload acceleration and the main pros and cons of this use case.
Artificial Intelligence applications in need of FPGA
- Machine learning is based on parsing data, learning from it, and using that knowledge to make certain predictions or “train” the machine to perform specific tasks.
- For learning, machine learning systems use different algorithms: clustering, decision tree, Bayesian networks, and so on.
- Deep learning is basically an approach to machine learning.
- An important part of it is neural networks, resource-hungry and complex learning models that were nearly impossible to use for real-world tasks before the first GPUs and parallel task processing was invented.
The process of using a neural network model can be split into two stages:
- The development, when the model is trained, and
- The runtime application, when you use the trained model to perform a particular task.
- For instance, let’s consider image recognition tasks. In order to be able to distinguish, for example, different types of road signs, a neural network model must process lots of pictures, reconfiguring its internal parameters and structure step by step to achieve acceptable accuracy on training sets. Then the trained model is shown a picture and has to identify what kind of a road sign is in it.
- This is where FPGAs come into the picture.
- To be successful, inferencing requires flexibility and low latency. FPGAs can solve both these problems.
- The reprogrammable nature of an FPGA ensures the flexibility required by the constantly evolving structure of artificial neural networks.
- FPGAs also provide the custom parallelism and high-bandwidth memory required for real-time inferencing of a model.
FPGA-based Acceleration as a Service
FPGA-based systems can process data and resolve complex tasks faster than their virtualized analogs.
And while not everyone can reprogram an FPGA to perform a particular task, cloud services bring FPGA-based data processing services closer to customers.
Some cloud providers are even offering a new service, Acceleration as a Service (AaaS), granting their customers access to FPGA accelerators.
When using AaaS, you can leverage FPGAs to accelerate multiple kinds of workloads, including:
- Training machine learning models
- Processing big data
- Video streaming analytics
- Running financial computations
- Accelerating databases
Some FPGA manufacturers are already working on implementing cloud-based FPGAs for AI workload acceleration and all kinds of applications requiring intense computing.
For instance, Intel is powering the Alibaba Cloud AaaS service called f1 instances. The Acceleration Stack for Intel Xeon CPU with FPGAs, also available to Alibaba Cloud users, offers two popular software development flows, RTL and OpenCL.