Know how AI chips are changing the world of computing including its current size, future developments, and its advantages. You will also come to know the leading companies in this field.
Artificial Intelligence chips are the new fad set in the world of computing. They are set to transform the world of computing, with their incredible processing speeds. Since AI is being used in almost all the areas like smartphones, self-driving vehicles, therefore AI chips have become so valuable now. There has been an exponential increase in the areas of conventional neural networks. There is a maximum of computations known as linear algebra computations, which is also called tensor math. Some of the input data is made into a vector and the vector is multiplied by the columns of a matrix of neural heights and the products of all these multiplications are known as 'Multiply Accumulate Circuits'. During the past twenty years, AI big data and fast combination have become the norm and ML have brought about deep learning. Currently, there are many AI chip startups like Graphcore, Effinix, Flex Logix and Cornami, which are involved in the business of startups.
Till 2017, VC's have tripled investments in AI chip startups and till 2019, it has reached approx 1 billion USD. Graphcore has received 200 million USD led by Microsoft and BMW, while others like Mythia Inc and Computing have raised millions in funding. Currently, many big firms like Qualcomm, Nvidia, and AMD have also entered this line.
AI software used to run on graphical chips. These chips have a very high capacity for parallel processing and a much larger capacity than CPU's. But more people are stating that microchips designed for deep learning can be much more powerful. A new AI chip Eyeriss, which leads to large computing and 10-10000 times higher efficiency. The chip is flexible to be used for adapted applications. It is based on field programmable gate arrays ( FPGA ) and application-specific integrated circuits ( ASIC ) and optimized for use. The new chips make high performance computing tasks like predictive analysis and query processing very fast. These chips are equipped with a large amount of data like text, images, and language. Neural networks are compressed up to 10 % of their original size and no increase in the error rate.
Currently, China is investing tons of money in the field of AI computer chips. Alibaba has announced that a new AI inference chip known as Ali-NPU in 2019. It is to be used in smart cities, logistics and self driving vehicles.
In the field of self driving cars, Tesla has announced that its chips will be installed and will be backward compatible. The new chip will be used to process 2000 camera images per second.
Which are the top AIchipmanufacturers?
The top AI chip makers in the market are listed below.
Inferential is a new chip designed by Amazon, which is used to deal with a large amount of data with lower latency. It is fully capable of handling power workloads, which is delivering thousands of teraflops per Amazon EC2 instance for multiple frameworks. It supports various kinds of data types, like INT-8, FP-16 and bfloat16. Another of the popular frameworks which it supports are Pytorch, TensorNetetc.
These chips are designed by Movidius, an Intel company and they are to be used for AI, vision and imaging applications. This chip is run by a pair of twin LEON4 controllers. The Myriad 2 family of processors are changing the capabilities of devices and giving world class performance.
Huawei has inaugurated two latest AI chips, Ascend 910 and Ascend 310. These chips are one of the fastest in the market and help in training networks, in very less time. Ascend 910 is used for datacentres and Ascend 310 is applicable for smartwatches and smartphones.
IBM has recently released a new 8-bit Analog chip, which has precision for its digital and analog calculations. This chip is used for testing neural net, which sees numerals with 100 percent efficiency. Since data goes between memory and processing, which takes up time and energy. This AI chip is based on phase change memory. This technique uses in-memory computing which doubles the accuracy and uses 33 times less energy. It is suited for low power environment.
Google has introduced one more chip, Tensor Processing Unit. The upgraded TPU goes into AI to carry the high workload. The original TPU was meant for the interference stage of deep learning, whereas the new version can handle training also. The company says that it takes one day to train machine translations system using 32 of the best available GPU's and the workload takes six hours at the top of eight connected TPU's. Google is currently operating this machine inside its datacentres.
Imagination technologies declared three new PowerVR GPU's (Graphics Processing Units ), which are directed for various product categories like neural networks for AI markets. It has a performance range of 0.6 to 10 tera opens / second. Multi score scaling up beyond 160 TOPS. These chips play a crucial role in bringing new capabilities in smartphones, smart cars, loT devices, and cameras.
AMD gave the world's first 7nm GPU with the name Radeon Instinct MI60. The company believes that GPU will power the next set of deep learning and AI applications high Performance Computing, graphical rendering and cloud computing applications. The chips are used for fast floating-point computing. This is used for GPU to CPU communication, which has increased. This is around 6 times faster. This is enabled by the AMD Infinity Fabric Link Technology. These chips are designed for high scale operations, where the 7mm technology by AMD is used to improve performance.
There are lots of benefits of AI computer chips. The main ones are being described below.
If you are not sending a lot of data into the cloud every two seconds, this means users will be able to access services offline. You will also be able to save data. If the analysis is done on this particular device, it will prevent the people from running the app, in order to pay for the servers.
With dedicated hardware like AI chips, leads to fewer chances of users data being leaked. So, it results in better privacy.
AI chips which are applicable for deep neural networks have the lowest latency. This means that the chances of them getting concealed are the lowest. The networks are hinted at their application.
Another advantage of AI Chips is the fact that it has a much lower power consumption. This enhances the speed of the AI processor to a great extent.
Since there are so many firms which are providing AI app services, it is very hard to choose the correct one, which will suit your budget and requirements. Appristine Technologies is one of the most sought after companies, which is providing world class AI development services, within reasonable prices. They have an exceptional team which is adept at providing the exact AI services within the most realistic time and deadlines. They serve clients in both India and the whole world.