Ever wondered what a neural processing unit (NPU) is? It is a micro-processor which is used especially for accelerating machine learning algorithms, usually by operating on predictive models. Predictive models could be random forests (RFs) or artificial neural networks (ANNs). A Neural Processor Unit can also be referred to as a neural processor.
It is vital to know that a Neural processing unit cannot be used for general purpose computing like the CPU (Central Processing Unit), which can do everything. Rather, it is a well-partitioned circuit consisting of all the control & arithmetic logic components needed to implement machine learning algorithms only.
Its specific purpose is to improve machine learning tasks and accelerate their performance. These tasks may include machine translation, images classification, object detection, and several other predictive models. Using it for tasks for which it isn’t assigned may result in poor performance.
A neural processor may be part of a large SoC (System on a Chip) or a part of a dedicated neural-network accelerator.
Over the past years, we have had tremendous breakthroughs in creating Machine Learning applications. These applications have been useful in our healthcare system, security, manufacturing industry, and the list goes on. With the help of these Machine learning applications, we now have:
- Enhanced health care with more accurate diagnosis and treatment
- Self-driving automobiles
- Security involves real time facial recognition and several others
It is safe to say that Machine Learning applications are moving our world to a higher phase. However, the drastic increase in the number of computations calls for a more specialized processor.
Implementing deep neural networks means executing a large number of multiply-accumulate operations, usually up to some billions & trillions of iterations. This large number of iterations stems from the fact that for every given input (say an image), a very large amount of MAC operations is involved.
Now, unlike central processing units which were very useful in the processing of some highly serialized instruction streams, Machine learning workloads seem to be more parallelizable.
Graphic Processing units also could not cope with the progress made in the Machine learning Industry neither could it scale well. For this reason, the Neural Processing Unit was adopted. It performs better and can cope with our rapid growth in the field of Machine Learning.
- It accelerates the computation of Machine Learning activities
- Uses lesser power and helps to utilize resources for Machine Learning activities.
Neural Processing Units (NPU) are not just in the books, they have been implemented in real life. Here are some real life implementations of the Neural processor: AWS Inferentia by Amazon, Huawei: Ascend, Alibaba: Ali-NPU, Neural Processing Unit (NPU) by Samsung, Neural Engine by Apple, Vathys, Tesla: FSD Chip, Brainchip: Akida (NPU & NPEs) and Wave Computing: DPU