AI modules: taking the market by storm
Around a decade ago society turned to cloud based computing to reduce client-side loading and potentially quicker turnaround on larger calculations. While this filled a need it started causing an issue regarding organizational security policies as data was continually being stolen with this becoming so prevalent that ransomware attacks are now a standard cyber-crime present in all sectors from manufacturing to hospital care and this is increasing by the day. Industries are doing a U-turn on their operational policies and now investing heavily on centralized onsite processing with layers of security between the core business infrastructures and the outside world. AI modules are helping to implement these policy changes.
What is an AI accelerator module?
An AI accelerator module is processing chips that are designed to process artificial intelligence calculations. An accelerator module takes sensor data that is provided by a CPU and pushes it to an AI processor which is designed to mimic the structure of neural networks and work. This makes them ideally suited for the simple but repetitive calculations that need to occur during AI utilization, meaning they are much faster for this application than a standard CPU that is designed for higher order calculations in a different throughput configuration.
AI accelerator modules come in three main solutions such as SMC (surface mount components), M.2 or mini-PCI cards. Additionally, they are also available in modular AI boxes that just need a power supply and sensors to work with the developer’s software solution. These solutions have progressively larger footprints in terms of space in the final solution, however they allow for upgrading of technology and up-cycling of end-of-life products. An AI module takes all the hard work out solution development by easy integration routes onto a main-board.
Manufacturing and Development Convenience
Thanks to modular nature and extensive options you can use an accelerator module in any AI application you need to without additional soldering stations in the manufacturing plant or the increased risk of part or board failure due the modular nature of the boards. This means that as you are assembling and manufacturing less parts overall the percentage of scrap will be less as all accelerator modules are all pre checked before being shipped. Should one fail you can just take it out of the assembly and replace it with another and vice-versa for the mainboard, ensuring you meet production deadlines.
AI modules at least the best ones come with SDK tools that abstract the hardware integration with the solution. An AI module that has SDK tools that allow developers to use containers to develop their AI solution is a powerful solution indeed and should be used in all applications. This is because abstraction enables containers to just be loaded via an SD card or I/O media very quickly before roll out. If there is ever a problem this can be quickly updated within the matter of seconds. For example, imagine that a new shopping solution that uses AI to list items put into a shopping basket is created and rolled out by a company, but for whatever reason the AI error tolerance is too low and adjusted in the solution, you should just be able to send a client new SD cards to update their solution at the fraction of the expense of sending staff to site. If your solution works internally then it will work externally no matter the adaptation. This is great news for small SMEs that provide smaller scale solutions as well as larger firms that need to update solutions in mass produced products such as self-driving cars.
