It’s been a fabulous century already for the Data Science industry with a majority of startups growing by innovating and selling Artificial Intelligence products.
While most of these startups are mostly selling AI software and solutions, there are a handful of others who are doing a coveted job, at least on the research, development, and manufacturing are concerned.
Let’s discover the underlying science and market forces driving the Artificial Intelligence Hardware industry in 2020-2029.
AI hardware: Integrated Processors/ Semiconductors
AI hardware is used today in PCs, virtual machines (VMs), mobile phones, gaming consoles, and everything that you can define under the Internet of Things or connected devices.
Digital architecture and automated networking systems need faster processes to make sense of all the AI algorithms. AI semiconductors are specifically created to ‘think’ about themselves and run on unsupervised cognitive intelligence.
The story of the semiconductor industry with AI at the core is very different from what it was for the linear computing models of the 90s or before.
Tools in AI Semiconductor family
Integrated Solutions such as simulation consoles, voice recognition sensors, vision control and capturing, camera sensors, drone automation, robotic arms, and fiber optics cables.
Very much connected to the processor family of hardware, AI accelerators are a special category of hardware machines that accelerate the adoption and application of AI operations.
These are mostly systems within AI frameworks that coordinate simultaneous computing and storage operations to optimize the interface between input and output.
Top AI accelerator tools include-
- Logic devices– CPU, GPU, and ASIC
- FPGA– developed in the middle 90s, Field Programmable Gate Array-based AI accelerators are the powerhouse driving machine learning and deep learning with programmable circuits.
- Memory and Storage
- Supercomputers, like IBM Summit and IBM Sierra.
- AR VR, like Oculus, Samsung Gear SM-R322, Live Planet, Nintendo Labo VR, and so on.
On-Chip AI Memory Hardware
In the third decade of the 21st century, AI applications can’t be bogged down by the persistent challenges in low-speed CPU, delimited Processing performance, and so on.
The solution to all these lies with the high-performance in-chip AI memory tools that are able to accurately predict and tune memory systems to deliver performance and bandwidth based on the output required.
AI edge chips are dominating every innovation coming to the center of the Hybrid Storage ecosystem. AI chips are advanced processors that are expensive to manufacture and difficult to train. These are classified as follows:
- SRAM: Static Random Access Memory
- DRAM: Dynamic Random Access Memory
With new silicon and system engineering shaping the AI hardware industry, it’s a great time to chase down the best Artificial Intelligence Course and build the future-ready AI systems with Flex Logix, IBM, NVIDIA, BrainChip, Syntiant, Ambient and so on.
Without the right hardware, none of the present AI projects would make any sense. AI hardware is much bigger, more complex, and more exciting than the software and Cloud-based industry which also remains the biggest marketplace.
It’s a multi-billion dollar economy after all!