A neural processor or a neural processing unit (NPU) is a specialized circuit that implements all the necessary control and arithmetic logic necessary to execute machine learning algorithms, typically by operating on predictive models such as artificial neural networks (ANNs) or random forests (RFs)
In Brain – Neural processes is the one that drive coordinated movement, attention, perception, reasoning, and intelligent behavior result from learning and memory.
So the computational mechanism that leads from graded signals to binary impulses should be the basis of learning and memory.
The NPU, or Neural Processing Unit, is built into the mobile processor to utilize advanced neural networks and provide new levels of vision intelligence. NPU helps the on-device AI, Bixby Vision.
An AI accelerator is a class of microprocessor or computer system designed as hardware acceleration for artificial intelligence applications, especially artificial neural networks, machine vision and machine learning. Typical applications include algorithms for robotics, internet of things and other data-intensive or sensor-driven tasks.
They are often manycore designs and generally focus on low-precision arithmetic, novel dataflow architectures or in-memory computing capability. A number of vendor-specific terms exist for devices in this category, and it is an emerging technology without a dominant design. AI accelerators can be found in many devices such as smartphones, tablets, and computers all around the world.
NPU are required for the following purpose:
- Accelerate the computation of Machine Learning tasks by several folds (nearly 10K times) as compared to GPUs
- Consume low power and improve resource utilization for Machine Learning tasks as compared to GPUs and CPUs
Multicore processor integrates neural processing unit
NXP Semiconductors N.V. expands its EdgeVerse portfolio with the launch of its i.MX 8M Plus application processor. This is the first device in the i.MX family to integrate a dedicated neural processing unit (NPU) for advanced machine learning inference at the industrial and IoT edge. It also packages an independent real-time subsystem, dual camera ISP, a high-performance DSP, and 3D GPU for edge applications.
The i.MX 8M Plus combines a NPU delivering 2.3 tera operations per second (TOPS) with a Quad-core Arm Cortex-A53 sub-system running at up to 2 GHz, an independent real-time sub-system with an 800-MHz Cortex-M7, a 800-MHz audio DSP for voice and natural language processing, dual camera image signal processors (ISPs), and a 3D GPU for rich graphics rendering.
The NXP i.MX 8M Plus can execute multiple, highly-complex neural networks simultaneously, such as multi-object identification, speech recognition of 40,000+ English words, and medical imaging. Developers can off-load machine learning inference functions to the NPU, said NXP, allowing the high-performance Cortex-A and Cortex-M cores, DSP, and GPUs to execute other system-level or user applications tasks.
Qualcomm Zeroth Processors
The final goal of Qualcomm Zeroth is to create, define and standardize this new processing architecture—we call it a Neural Processing Unit (NPU.) We envision NPU’s in a variety of different devices, but also able to live side-by-side in future system-on-chips. This way you can develop programs using traditional programing languages, or tap into the NPU to train the device for human-like interaction and behavior.
Zeroth processor function is striving to replicate the efficiency with which our senses and our brain communicate information. Neuroscientists have created mathematical models that accurately characterize biological neuron behavior when they are sending, receiving or processing information. Neurons send precisely timed electrical pulses referred to as “spikes” only when a certain voltage threshold in a biological cell’s membrane is reached. These spiking neural networks (SNN) encode and transmit data very efficiently in both how our senses gather information from the environment and then how our brain processes and fuses all of it together.
Examples
Real life implementations of Neural Processing Units (NPU) are:
- TPU by Google
- NNP, Myriad, EyeQ by Intel
- NVDLA by Nvidia
- AWS Inferentia by Amazon
- Ali-NPU by Alibaba
- Kunlun by Baidu
- Sophon by Bitmain
- MLU by Cambricon
- IPU by Graphcore
- Ascend by Huawei
- Neural Engine by Apple
- Neural Processing Unit (NPU) by Samsung
NPU – Patent – URL : https://patents.google.com/patent/US8655815B2/en