One of the biggest challenges in AI commercialization is its computational speed for integrating a set of data structures and implementing the training and inferencing of the particular module. So, the primary aim of AI acceleration is to increase the computational speed and frequency in real applications and reduce the size of the modules and data structures. However, embedded software development with AI integration involves two different approaches. They are
- Choosing the proper hardware for the embedded architecture to accelerate AI and
- Developing a design that is appropriate for the model which lowers the computational speed.
In today’s scenario, most companies use these approaches together in the cloud to provide the highest possible interference. Hence, in this blog, we will look into the two different kinds of AI acceleration involved in embedded systems and how they are incorporated into the embedded application.
Types Of AI Acceleration:
There are two types of AI acceleration: hardware acceleration and software acceleration. Hardware acceleration is said to be the primary one as it involves computing solutions to the problems until a reasonable computational time. However, in recent times, the neutral network is also given much attention, making software acceleration commonplace.
Types involved in hardware acceleration:
- Selecting the processor
- Usage of external GPUs
- Addition of AI accelerator in ASIC
- Implementation of FPGA
There are many ways to implement AI acceleration in the hardware. However, implementing AI acceleration in hardware was impossible back then, and every process was made on embedded software. So, the only way for AI acceleration is where the developers used to process many computing resources on the AI problems and allow it to optimize by themselves.
In today’s world, there are many types of AI accelerators which are implemented directly into the hardware. The most popular ones among them are AI accelerator ASICs, FPGAs, and many more.
Types involved in software acceleration are
- Sparsity removal
- Optimization of workflow
Quantization is one type of software acceleration in which a large set of mathematical techniques is involved to convert a large data input to a smaller output set.
Another type of software acceleration is the pruning, whose primary aim is to reduce the model size by removing some layers or neurons. So, the AI acceleration by pruning is processed by eliminating the neural network weights which have less significance.
So, the least significant neurons refer to particles with lower thresholds. Thus, by lowering the model size, you can reduce the computing operations during the interference. It is also to be noted that pruning can also reduce the iteration number by adjusting the layers and neuron’s number dynamically.
Sparsity elimination is another type of software acceleration where the elements are removed, which is zero-valued and does not provide quality input for the model’s accuracy. Thus, this simple logical check allows you to go through the values stored in the register. Hence, removing the zero-valued elements reduces the overall memory and processing requirements.
Hence, hardware and software considerations should be taken together for a highly-effective acceleration. So, when you need a redesign for your embedded system with an AI acceleration approach Sunstream for standard embedded software development services.