5 Top High-Performance Chips For Machine Learning
Machine learning has gained enormous recognition in the past several years. It has been in the marketplace for more than 30 years, and finally, experts are creating various high-performance hardware to catch up with the demands of these power-hungry algorithms. The chipset manufacturers with each passing year are trying to make some revolutionary things that boost the performance of the hardware ever than before.
The high performance AI chips emerge conquering than ever before. These chips play a significant function in growth as these can be used in various electronic devices to boost performance and make them increasingly independent. These AI chips will be used in the building of smart homes to make electronic devices more intelligent and in other technologies. The High-performance chip integrated with AI technology is a new generation of microprocessors which are specially used to make artificial intelligence-related jobs quicker.
Here we have listed some of the top hardware innovations that will transform the digital world in upcoming years:
AWS Inferentia
The machine learning chip is one of the latest AI chips in the marketplace. The chip was first of all announced by Amazon ‘Inferentia’ during the re Invent meeting in Las Vegas. The chip delivers high-performance results and that too at very- low costs. The chip is designed by an Israeli company owned by Amazon- Annapurna Labs. The main intention behind making such a powerful chip is to make the heavy workloads easier while requiring lower latency. The smarter chip is designed for inference, which helps in finding the vast data.
Features
· It can also handle power workloads
· Supports up to 128 TOPS
· Popular frameworks that it is compatible are TensorFlow, Apache MXNet, and Pytorch
· Supports FP16, BF16, and INT8 data types
· Extremely- low cost
IBM’s 8-Bit Analog Chip
The International Business Machines Corporation, referred to as IBM, has finally developed an 8-bit Analog chip. The multinational company has developed this new hardware that brings control efficiency and enhanced training for AI projects. The chip is used in various neural tests and is providing an accurate 100% efficiency and performance. The 8-Bit Analog Chip of IBM’s is based on propaganda of phase-change memory. The best element is a matter that can go through phase changes in reaction to electrical current.
Features
· 100 percent efficiency
· Phase-change memory
· Consumes 33x less energy than other digital hardware’s
· The best option for low-power environments
· Can be used in various applications
Google Tensor Processing Unit
Well, this unit was introduced in 2016 by Google for simplifying the matrix multiplication process in neural networks. It is also known as TPU, and it is an application-specific in-corporated circuit (ASIC) that is developed by the tech giant for machine learning. The TPU is made of a multidimensional array of metrics for a high volume of low precision computation. However, Google TPU consists of various processing elements that are used for parallel computing procedures.
Features
· Accelerate the performance of linear algebra computation
· It is used for ML applications
· TPU can be used for complex neural network models.
Power VR GPUs and AI chips By Imagination
These three-point units were introduced in a recent announcement by Imagination Technologies. These three PowerVR graphics processing units (GPUs) integrated with AI technology will be designed for a variety of groups of products. These help in boosting the efficiency of neural networks for AI markets. Power VR GPUs and AI chips have a good performance scope of 0.6 to 10 Tera operations per second (TOPS). These chips will be mostly used in smart cars, cameras, smartphones, and many more devices. These chips are licensed by world-leading companies and help in delivering the best performance solutions.
Features
· Boost in performance
· These chips are a perfect blend of flexibility and performance optimization.
· Prototyping is easy with this chip
· Offers power-efficient and flexible solution
Qualcomm AI Chips
In 2019 during an event in Spain Qualcomm, announced a new chip that will be used for speeding up AI-related work. The central intuition behind creating such a high-performance chip is to diversify ahead of its stronghold in mobile phone chips and stay ahead in this fast marketplace. As we know, they are one of the best players in the chip-making procedures for mobile phones. The chip designed by Qualcomm’s for AI is designed will help developers in neural network models and also optimizes various other performance issues on the devices related to Snapdragon.
Features
· Offers enhanced consistency
· Provides resourceful use of network bandwidth
· Real-time responsiveness
· Improved privacy
CONCLUSION:
In a nutshell, the above-listed hardware innovations change the digital world for good and make many procedures simple. Several companies are continuously conducting their research in developing AI-based customized solutions at different levels of power consumption used by these chips. Overall, deep learning has picked up enormously from the past couple of years, and experts are continuously using it extensively in abundant areas — from building digital AI assistants to smart vehicles that work automatically. When deep learning is combined with machine learning, you can deal with big data sets that need established chips for crunching big numbers.
