[WIP] CHERU::SecondaryBrain

What are the common activities that we do on the computer?

• Read articles, books
• Listen to music and watch videos
• Write blogs, opinions
• Use Internet to communicate with other people or other computers

But where do we keep all the information consumed? Our brain. But at the rate we consume the information, it becomes impossible to verify whether the information is true, retain all the information in our memory. No you might say, we do store documents like PDFs, pictures and docs in computer. Yes we do, but there is a huge difference in the way how our brain store and how we store the information in computer.

I said 'how we store the information in computer', because the computer does not store information by itself. What do I mean by this? The way our brain stores the information is in the form of network of linked concepts, unlike the computers where we store the information in the form of documents and images. Because of the difference between the organization of contents by our brain and the computers, we are redundantly storing information. We are under utilising the facitlities offered by the computer. The computer can do much more than what it is doing for us now. The computer can act as a secondary brain. Let me illustrate the idea with a paper instead of computer, and explain why it is uniquely suited for acting as secondary brain.

Let us say we are going for shopping long list of groceries. What do we do? We list down the items in a paper. Because it is little difficult to remember all the items in memory. I admit that, some of us can remember all the items and with some memory exercises almost all of us are capable of doing the same. But is there any use in remembering those list of groceries in memory? Or is there a point in spending time on memorising that list?

Let us take a complex example. In blindfold chess, the players are blindfolded and they say aloud the pieces to move and where to move. There is a third person who actually carryout the moves. No think about how players have to keep track of the piece positions and simulate the moves before shouting out the next move. Compare that with how easy it would be to look at the board and carry out the calculations of moves.

In the above examples, we offload the unnecessary things onto outside elements like paper and chess board. leaving room for more important things in brain. I think it is safe to assume that now you might have understood the usefullness of very simple tools like papers and chess boards. Imagine what computer can do, and what we can do with computers. Unlike papers and chess boards, the computer can carry out calculations on their own(computer play chess too), be as simple as they seem when compared to our brain. This makes it an effective tool to act as a secondary brain. What I mean by secondary brain will become apparent as we travel along.

This document will serve as an informal specification of how I image the secondary brain might work.  See you on next post.

#CHERU::SecondaryBrain

[WIP] Deep Learning hardware for the Commons

Deep learning required huge amount of processing power. Building such in all households is infeasible in near future (even if there is a breakthrough in hardware technologies for ML, it will mostly be costlier than already available options).

All ML systems consists of two phases, training and prediction(or classification, in general application). Fortunately, actual prediction process requires lesser processing power than training.

It might be useful to cooperatively design/build and run a powerful machine for training purposes and then use the model developed from training in less powerful machines like raspberry-pi.

Note that the training process need not end once and for all. We can collect new data sample after deployment, even though not every application area cater to this process, but certainly there are areas we can harness this.
 
Large System with all the rings and bells.

A compute cluster with heterogenous nodes, nodes with different combinations of cpu/gpu, nodes with different kinds of FPGA based accelerators (along with tensorflow model graph to FPGA compiler)

FPGA can also be used to run trained models for deployment.