NEURAL PROCESSING UNIT (NPU)

A neural processing unit (NPU) is a type of microprocessor designed to speed up complex computer processes. The NPUs ability to offload tasks from the CPU and GPU allows for faster and more efficient operation of the entire computer system.

NPUs function by mimicking the structure and efficiency of biological neural networks. Unlike general purpose processors, NPUs are optimized for parallel processing, pipeling, data reuse, sparsity, matrix operations etc. which are essential for complex mathematical computation required for deep learning, neural networks, facial recognition, voice assistance, artificial intelligence, machine learning, deep learning and complex data analysis.

There are several types of NPUs and they are; graphics processing units (GPUs), tensor processing units (TPUs), Neural network processing units (NNPUs), application specific integrated circuits (ASICs) and field programmable gate array (FPGAs).

Graphics processing units are not really by design function NPUs but some GPUs have evolved to function like NPUs making them suitable for complex mathematical computation application.

Tensor processing units (TPUs) as Google custom designed NPUs are optimized for large scale machine learning and other complex computations and they are primary used in Google data centers.

Neural network processing unit (NNPUs) are dedicated NNPUs used for accelerating neural network computation.

Application specific integrated circuits (ASICs) can be optimized for specific artificial intelligence, machine learning, deep learning and complex computational mathematical workloads with assured high performance and efficiency.

Field programmable gate array (FPGAs) can also be programmed to perform specific artificial intelligence, machine learning, deep learning and complex computational mathematical workloads with assured high performance and efficiency.

The basic components of a typical NPUs are; processing elements (PEs), memory hierarchy, interconnects, data flow management, power management, software stacks and other components.

The processing elements are the core components of an NPU and they are responsible for executing complex computational algorithms. Processing elements sub-components are; arithmetic logic unit (ALU), multiply-accumulate (MAC) units and tensor processing (TPUs) unit.

The interconnects units are used to connect PEs, memory and other components within the NPU.

The data flow management unit is employed or used by the NPUs to ensure that data is properly routed and processed. This task is accomplished by using data flow controllers, scheduling algorithms and other techniques.

The power management is implemented by the NPUs by using dynamic voltage and frequency scaling device and power gating units.

The software stacks for the NPUs includes the necessary software frameworks like machine learning frameworks for machine learning and so on and NPUs specific software.

The other components included in an NPU are digital signal processing (DSP) units, cryptography units and other peripheral interfaces.

The advantages of NPUs are; NPUs ensures accelerated performance by reducing processing time and increasing throughput of complex computational workloads. NPUs are energy efficient, scalable, flexible and above all are suitable for highly complex and large scale applications.

The disadvantages of NPUs are; NPU are very expensive, they have limited flexibility, they are very dependent on software optimization using the necessary software frameworks and are not widely available or supported by all systems and frameworks.

The applications of NPUs are diverse and they include; deep learning, natural language processing, computer vision, autonomous vehicles and a host of other applications too numerous to mention.

The future of NPUs depends on the following; increased adoption, advancement in architecture such as 3D stacked memory and novel interconnects, more focus on AI use and the growth of demand for software optimization.

 

SOURCES:

• Neural networks and deep learning by Micheal A. Nielsen.
• Computer organization and design by David A. Peterson and John L. Hennessy.
• Designing learning systems by Chip Hugen.
• Deep learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville.
• Artificial intelligence: A modern approach by Stuart Russel and Peter Norvig.