When the world is all active and adapting itself with the new tech innovations, there is still space for the latest developments. A much closer look at the current Artificial Intelligence applications will assure the new set in technology, AI chips. From cars, smartphones, to healthcare, and every other field growing with AI, AI chips have built their position in the world so well. They are specifically designed to transform the world of computing with their intensive processing capabilities and speed.
Bill Dally, Nvidia’s chief scientist states the need for focusing on more specialized chips which can overshadow the GPU chips and are good at handling various computations. The increased demand for AI chips has convinced VCs to spend around 1 billion USD. Companies BMW and Microsoft have spent around 200 billion to the Graphcore to develop more AI-enabled applications including AI chips. According to Allied Market Research, the global AI chip market is predicted to reach $91.185.1 million by 2025 with a CAGR of 45.2% from the year 2019 to 2025.
What AI chips do?
Machine learning has come a long way and has huge capabilities from facial recognition, detection, and language translation. With AI and adopted technologies, applying filters, photography, and high definition cameras in our phones look normal. With the introduction of AI chips, this work could be done better, faster that too with less power consumption. The newly launched microchips like Eyeriss is way faster and are flexible enough to be used with adapted applications. It can perform predictive analysis, equipped with huge data, and process queries at a double rate. The chip is based on the application-specific integrated circuit(ASIC) and field-programmable gate arrays(FPGA) and is designed to perform error-free computing tasks.
Edge AI chips: A boon for enterprises
Has it ever crossed your mind that if AI processors can be used in smartphones and other applications, why not in enterprises? For example, a drone equipped with smartphone SoC AP has the ability to navigate and avoid any obstacle that too with no network connectivity. To address high computational power, flexibility, and less power consumption, AI edge chips are developed.
The edge AI chips are advantageous in many ways. They are small enough to fit on a USB stick and draw less power between 1 to 10 watts. Using these chips, enterprises can develop better possibilities mostly in regard to the IoT applications. The edge AI chips can be used to analyze and collect data from various connected devices. These collected data can be put into action without investing cost and complexity of shifting huge data into the cloud. Some of the maj0r benefits of AI chips are:
1. Low connectivity
The major advantage of AI chips is they demand low or no connectivity with the device. Where data processing requires device connectivity in the cloud, AI chips can be used to process data between devices without any connectivity. Taking an example, it is difficult to maintain connectivity with a drone as they are operated at far distances. Drone with AI-enabled chips and embedded machine learning can be easily used for purposes like navigating to places without an internet connection.
2. Low latency requirements
Performing AI computations at remote data centers require the lowest latency. Using edge AI chips reduces the latency to nanoseconds and the devices can easily function i.e process, collect and process huge data virtually. For example- Robots can easily capture orders from factory settings and quickly respond to human instructions.
3. Power consumption
The low power AI chips can be used in devices with small batteries to perform AI computations. For instance, ARM chips are inculcated in respiratory inhalers to analyze lung capacity and medicine flow in the lungs. The data is then sent to a smartphone application which helps the doctors to provide the required treatment to the asthma patients.
4. Processing huge data
We all know IoT devices produce a huge amount of data. Sending such huge data into the cloud requires immense cost and complexity but implementing machine learning processors on the endpoints makes the work easy. Using AI chips, the devices can easily analyze data and transfer only the useful data to the cloud thus demanding less cost and complexity. For example- The security cameras produce 2500 petabytes of data every day. These cameras could be embedded with VPUs and the analysis of digital images will be easy.
5. Privacy and data security
Cloud is a secure platform for storing data but collecting and moving data into the cloud could sometimes end up with enterprises facing cyber threats. When large data is processed locally, the chances of security are increased and the risk of enterprise attacks is lowered. With AI chips it is possible to store a huge amount of data locally preventing any kind of security risks and ensuring privacy.
Security cameras with ML processes reduce privacy risks and send only the accurate part of the video by analyzing the whole video and sending the necessary parts to the cloud. AI chips, when used to perform recording activities, recognize a broader amount of voice commands offering less audio to be analyzed in the cloud making the task easy.
Some of the top AI chipmakers around the world
As discussed above, AI chips are growing to the bee’s knees and the coming generation will witness more such tech advancements. Here are some of the topmost AI chip makers in the global market–
Google TPU
Tensor Processing Unit, one of the AI chips introduced by Google to carry high workloads. It takes a day to train a machine translation system using 32 of the best GPUs available but with eight connected TPUs it takes only six hours. Currently, the machines are operated inside Google data centers.
Huawei’s Ascend 910 and Ascend 310
The two chips Ascend 910 and 310 are inaugurated by Huawei and are used for training processes within the least hours. Ascend 310 is used in smartwatches and smartphones whereas Ascend 910 is used in data centers.
IBM‘s 8-bit Analog chip
The 8-bit Analog chip inaugurated by IBM is the latest offering to the tech world. It is used for digital and analog calculations and also for testing neural networks. The chip uses phase-change memory, uses 33 times less energy, and uses in-memory computing to double numeral accuracy.
The vast usage of AI chips will bring new opportunities and significant changes in the world. From producers to consumers, all of them will be benefited from the increased AI and ML production and adoption. With AI chips catering abundance of features like voice assistance, security locks in phones, and clicking pictures without requirements of internet connectivity, it is easy to adopt new practices in the real world. The AI chips can be used by enterprises to take their IoT applications to a more improvised level. They can be used in developing marketing, logistics, culture, and manufacturing thereby interpreting and collecting vast amounts of data. Bringing the digital intelligence to the device, AI chips are renovating the existential potential of the applications.
Key Takeaways
- The AI chips are much better than traditional CPUs for running in the real-world applications and training algorithm.
- The use of AI technologies in various sectors and enterprises is increasing every day. The AI chips are becoming a growth driver for enterprise work performances.
- Since, AI runs basically on algorithms, computing power, and data they can be used to manage a larger volume of data and handle complex calculations as well as training devices
