Artificial Intelligence is the development of New Age Electronics and is growing and entering into all kind of market and product spaces.
The Devices empowered with these technology gives tremendous scope to gather learning and improve behavior response of the device in runtime. With this improvement capability in runtime as a key feature enabling it to capture new spaces of deep learning and making human based error to zero.
Machine Learning: Is a Learning Sequence of Artificial Intelligence that gives machines the ability to learn and improve without the help of humans or new programming.
At present many problems are present in AI and ML technologies. In Artificial Intelligence some problems are : –
- Unexplainable Results.
- Stability and High Processing Requirements.
- Result Full Autonomous.
- No Backtrace if Conflicting Results.
In Current Machine Learning Methodology, there is
- No Recovery if wrong learning is pushed.
- Unpredictable learning – One cannot predict what the learning is actually there inside system
- Learning cannot be reversed – Once wrong learning is pushed, one cannot rollback learning or delete one of the learning.
Everything is like a block box.
Here you go, let’s discuss this and understand –
No Recovery: – Sometime machine data corruption occurs because of any disturbance or noise. In a worst case, while data transfer from one point to another point and in between this process, data gets modified by noisy environment, It impacts the entire working state of an AI engine. Because of no recovery, some data lost and did not get the reliable results. It is a serious problem. It disturbed the real time learning results. Because of this, it is an impossible task to make a high reliable AI Infrastructure.
Unpredictable learning – When it comes to identifying insider threats, the fundamental challenge is how to determine when data access appears out of the ordinary for a typical user or system, and of those instances, which ones are dangerous versus merely unusual.
Today, a lot of solutions serve exposure to noisy environment and hazards, it’s nearly impossible to find the real red flags that indicate you’ve got a problem.
Unexplainable Results– Sometimes AI gives uncontrollable and unexplainable results. When data or state or learning coefficient are corrupted because of noisy environment then AI can gives the random results. These results are unpredictable and unexplainable and is not possible to filter out from other results while running the system.
Stability and High Processing Needs – AI is a high processing technology. And needs very high stable infrastructure. But because of noisy environment, it also lost the stability and high processing features. Due to this, AI is a kind of unstable and unpredictable to be applied at fully autonomous high reliable infrastructure requirements.
Fully Autonomous Result: – AI devices are fully autonomous. Because they work automatically, we cannot control the AI device results. In between the working process, noise is affected to the system. But it is fully automatic that’s why it gives the corrupted result but we cannot control the device and find the problem. It is also a major problem.
No Backtrace if results are conflicting : – A back trace is a list of the function calls that are currently active in a thread. If AI devices are working in the continuous form and give the results but because of disturbance and noisy environment Data/ State/ Coefficients are corrupted and give the conflicting results, We cannot predict the actual results. For example, if device give an important information (like location, atmosphere etc.) and suddenly give the conflicting results due to noise then we can’t predict that it is correct or wrong.
Learning cannot be reversed – In the present ML technology, device learning is in the continuous way. It is automatic and gives the results. We cannot go to reverse on even if we predict a wrong learning is fed into ML system. Most of the time, the Noise remains totally hidden from the system software and impacts the final learning.
To Resolve these basic types of problem, GreenIPCore presents noise resistant SOC components.
With this new noise resistant technology, We can construct a noise resistant and a highly stable SOC which can work in all type of environment and can give a stability to current AI infrastructure to make it reliable and safe to be applied at autonomous spaces. These products can help to ride on new age electronics into more robust, reliable and autonomous products of the future world which can resist the electromagnetic Noise and increase the overall satisfaction of safe and more reliable product line.
Noise Resistant Digital IPs have some more advance features. They are :-
- Noise Resistant
- High Stable IPs
- Fully Safety Compliant
- Fail Safe Recovery
- Equivalent gate Count
- Good Operating Frequency
- Fully Protocol Compliant
And with these advance features, following are the improvements we can see –
Noise Resistant: – Our IPs have noise resistant feature which protects the chip from the any type of noisy environment. Because of noise resistant feature, noise will not effect to the data at runtime or any time. It will protect system from all the noise and give the stable and correct data. It detects the noise and give this information in the form of a signal or message but don’t conflict the data. For example, we are doing some long important calculation and suddenly any type of noise produced in surrounding can interrupt the calculation of some intermediate state and corrupts the intermediate results, resulting in overall result variations. Our New Noise Resistant technology can help out to solve this.
Recovery problem will be resolved with this technology As Data will not be corrupted by the noise. Data will not conflict due to noise so back trace is not a problem. This problem is also solved. Noise resistant device is the way to go for the fully autonomous devices.
Highly Stable IPs: – Noise resistant components which can be implemented inside already existent SOC/ IPs to make them strong from inside and make them safe, secure, noise resistant and Robust. These are error and glitch-less IPs which are more reliable. For example, device gets corrupted by the noise and gives out corrupted results, because of noisy environment .However , if IP is noise resistant in nature then IP is protected by these types of problems.
By implementing these methods , we can add Stability with High Processing requirements to the AI and ML infrastructure.
Fully Safety Compliant: – Our IPs are fully safe from the complaint. These are safe, secure and stable from any type of noisy environment. They protect the system and save the data from the noise. For example, fully automatic devices are also working properly because these IPs are free from the errors and glitches. No need of recovery and back trace features as they do not give the conflicting results.
With help of Availability of Noise Resistant IP from GreenIPCore, one can built a very stable SOC.
These are very stable and which make the safe, robust, reliable and best fit for autonomous products like AI and ML applications.
Artificial Intelligence and Machine Learning technologies, these are very useful only if we can make them Autonomous, Reliable, Safe and Secure.