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TPUs are only one part of this eye-opening presentation. Skip to page 28, where Jeff starts talking about: * Using reinforcement learning so the computer can figure out how to parallelize code and models on its own. In experiments, the machine beats human-designed parallelization. * Replacing B-tree indices, hash maps, and Bloom filters with data-driven indices learned by deep learning models. In experiments, the learned indices outperform the usual stalwarts by a large margin in both computing cost and performance, and are auto-tuning. * Using reinforcement learning to manage datacenter power. Machine intelligence outperforms human-designed energy-management policies. * Using machine intelligence to replace user-tunable performance options in all software systems, eliminating the need to tweak them with command line parameters like --num-threads=16, --max-memory-use=104876, etc. Machine intelligence outperforms hand-tuning. * Using machine intelligence for all tasks currently managed with heuristics. For example, in compilers: instruction scheduling, register allocation, loop
nest parallelization strategies, etc.; in networking: TCP window size decisions, backoff for retransmits, data compression, etc.; in operating systems: process scheduling, buffer cache insertion/replacement, file system prefetching, etc.; in job scheduling systems: which tasks/VMs to co-locate on same machine, which tasks to pre-empt, etc.; in ASIC design: physical circuit layout, test case selection, etc. Machine intelligence outperforms human heuristics. IN SHORT: machine intelligence (today, that means deep learning and reinforcement learning) is going to penetrate and ultimately control EVERY layer of the software stack, replacing human engineering with auto-tuning, self-improving, better-performing code. Eye-opening. |
Ah so it appears they're advocating using neural networks as index functions to sorted arrays (hashmaps are simply sorted by hash instead of by something in the data).
So what they do is they take a FIXED set of data that you want to quickly lookup in, already sorted, train a model (2 layer 32 width, relu activation is one architecture, but they also train sequences of models, HUGE changes to error (as the cost of max and min error are huge, you minimize max error rather than average error)).
They have the following brilliant insight : an index over a database (which gives the position of the data given the search key) is a CDF (cumulative distribution function) ! That's brilliant ! Of course it is !
And of course, this is Google. Once you have an index trained (which is a linear operation), you can translate the neural network model directly into C++, and compile it into machine instructions that don't depend on anything like tensorflow libraries. The resulting code can be pasted into anything you want. This may work fast, but seems less then entirely practical ... although I guess you could do the same in Java far easier and you could just include that code.
Paper here: http://learningsys.org/nips17/assets/slides/dean-nips17.pdf