Why You Need an AI Accelerator for Training: A Deep Dive
AI technology is moving incredibly fast. And so is the adoption of it: recent statistics show that 37% of businesses and organizations use AI in some form. As it becomes more powerful and complex, a deep learning training accelerator can get the most from artificial intelligence applications.
Deep learning is designed to work almost like a human brain, with several “layers” that influence each other to arrive at a final output. It also requires training the AI on vast amounts of data, so it can identify and recognize patterns and make predictions. Multi-modal learning models are rising, combining different datasets including text-based (natural language processing) and image-based information.
AI accelerators for training can increase computing power, while training accelerator software can also deliver more flexibility in tandem with hardware. However, you’ll need the support of an expert like us to ensure you’re getting the most from your AI, especially when there are different types of inputs.
Let’s take a closer look at why to consider an AI accelerator…
What are the benefits of a deep learning accelerator processor?
With the vast amounts of data available to analyze and the sheer volume of calculations involved, the AI computing process can use up a lot of energy and memory – not to mention time.
This is where a deep learning training processor comes into the picture. These processors are specifically designed to handle AI workloads, including parallel deep learning (partitioned across multiple machines for efficiency.)
With the growing number of hardware accelerators, from graphics processing units (GPU) to application-specific integrated circuits (ASIC), there is a growing need for software stacks that can optimize them. These accelerators can dramatically speed up real-time applications such as security, for example.
Here are a few key functions of a training accelerator, paired with the right software:
- Increased computing efficiency. AI requires a lot of processing power to come up with an output in a reasonable amount of time. As demand for quicker results increases, the systems need to be up to the task. A deep learning training processor delivers this advantage, and also increases the memory required to perform functions.
- Easier scalability. Running algorithms on parallel systems can be a challenge. However, with the help of a deep learning accelerator processor, they can be efficiently run along multiple cores.
- Energy savings. Because AI accelerators boost computing power, they also reduce the amount of energy required to produce results. These accelerators can reduce power usage exponentially, reducing utility costs – and reducing the heat from performing rapid calculations.
- Adaptable architecture. Various AI training accelerators can be added depending on the need, allowing the system to run deep learning, analytics, and other functions across shared networks.
The SynapseAI® software stack offers access to reference models, kernel libraries, and containers to meet their specific needs as well as collaborate. Learn more about multi-modal training models, as well as its latest AI inference solutions.