As a sub-set of Artificial Intelligence, machine learning is perfectly designed to execute tasks related to AI. Companies looking to maximize profits have already embraced the technology. This will empower them to pave way for the summit of success, in the form of Machine Learning Chips. The technology used by this mini genie is quite unique. It uses exclusive computational methods and algorithms to instill computers with required data. As a result, computers will think the way animals and humans think in certain situations. By augmenting the number of trials, one can pick up the way machine learning algorithm performs.
However, machine learning models deal with large amounts of data at a time. Its use has become pretty extensive. In fact, we can use Machine Learning models for things as diverse as autonomous vehicles or digital colloquial. And, for such advanced tasks, it needs very powerful chips, so that it can chomp large sets of data. This post is going to discuss quite a few advanced Machine Learning chips that can accomplish huge computations.
This is one of the latest in the AI chips. Amazon announced the emergence of Inferentia in the re:Invent conference in Las Vegas. Exclusively designed to deal with heavy workloads, Inferetia would make space for lower latency. When it can handle inference (the process-trained ML model to detect patterns in huge data) in the best possible manner, it can also pick up heavy power workloads, delivering large number of teraflops for multiple frameworks.
Ascend 910 and Ascend 310
Two new Machine Learning chips, Ascend 910 and Ascend 310 were announced by Huawei at an ITC industry event. Made for data centers and internet-enabled devices, they are one of the most powerful chips for edge computing outlines. These Machine Learning chips will also bring forth huge data in a very short amount of time. Additionally, it will help drill networks in an efficient time frame.
Bit Analog Chip
When it comes to presenting first-hand hardware that makes way for power efficiency and enhanced training for AI projects, the need for an advanced Machine Learning chip has always been on board. Additionally, bit analog chip, with an 8-bit precision, will now check a simple auditory net that pins down numerals with 100 percent effectiveness. Data always tends to ply between memory and processing. Therefore, a smooth delivery can be a challenge. This Machine Learning chip by IBM, based on phase-change memory, overcomes these trials quite effectively.
Tensor Processing Unit (TPU) had already been introduced by Google in 2016, and now it’s in its third generation. Upon upgrading to an advanced standard, this exalted version goes deeper into AI than the initial accounts. Additionally, it manages complicated workloads with high-end precision. However, with the recent add-ons in the chip, it will tamper down Google’s dependency on said chips. Here, it is worth mentioning that the previous TPU marks the inference phase of machine learning, while the new version can deal with training too.
Myriad 2 Machine Learning Chip
Designed by Movidius, a well-known Intel company, these chips are used for some of the most pushy as well as strident AI, vision, and imaging claims, encompassing both low power consumption and improved performance. These high-end processors are almost remodeling the competences of devices and providing state-of-the-art performance at a matchless price proposal. Intel’s Myriad 2 Machine Learning chip is regarded as one of the most active and lively radiation beams on the planet.
Qualcomm AI Chips
In chip making for mobile phones, Qualcomm has always been in the list of top players and now it has released two new structures to aid smart visual applications for IoT frameworks. The compactly packed 10-nm FinFET-based chip can run facial recognition, trail mechanical equipment in Industrial IoT, and do many more wonders that involve high-end accuracy and meticulousness. Also, Qualcomm’s Neural Processing SDK for AI is exclusively fabricated to help developers perform on more than one neural network model together. Apart from saving huge effort and time, it, with the aid of Snapdragon, happens to enhance performance of trained neural networks on electronic devices. Moreover, it does a lot of heavy lifting needed to run neural networks, makes advanced model adaptation & implementation possible, and more.
PowerVR GPUs and Machine Learning Chips
In a recent affair thrown by Imagination Technologies, emergence of three new PowerVR graphics processing units (GPUs) has been announced. They will aim for different groups of products such as neural networks for Artificial Intelligence markets. With a performance scale of 0.6 to 10 TOPS and multi-score stepping up above 160 TOPS (Tera Operations Per Seconds), these chips play an important role in initiating new computing aptitudes in smart devices such as cameras, smartphones, cars, and more.
AMD GPU Radeon Instinct MI60
The world’s first 7nm GPU named Radeon Instinct MI60 by AMD is again a revolutionized brainchild. With an outstanding 1TB/sec of memory bandwidth, GPU would command over the next generation of machine learning top-end graphical rendering and cloud computing uses. The chips also come with the ability for super advanced computing performance.
According to Allied Market Research, the global market of machine learning chip market will grow at a significant CAGR from 2018-2025. This in part is due to the advent of quantum computing and rise in the number of machine learning applications. Additionally, the demand for trending Artificial Intelligence has fueled the growth of global machine learning chip market. On the other hand, lack of skilled workforce has slightly curbed the growth. However, there is a growing demand for smart homes & smart cities. Additionally, there is a rise in efforts to make more human-like robots and huge craze for IoT across the globe. These elements have almost downplayed the factor and created multiple opportunities in the segment.
To conclude, we can state that machine learning chip market is expanding quite fast. Eventually, it will flourish more in the years to come.