GPUs powering the AI revolution

Ganapathy Raman Kasi*, Muthiah Annamalai+

[This article originally appeared in the 2017 Tamil Internet conference, UT-SC, Toronto, Canada, magazine ]


The current hot trend in AI revolution is “deep learning” – which is a fancy way of talking about multi-layered convolutional neural networks; this field of study has heralded a new age in computing extending human capabilities by automation and intelligent machines [1].

These neural networks aren’t the same as neuron networks in your brain! We are talking about artificial neural networks which reside in computers and tries to mimic the biological neural network with its synapses (connections) of axons, dendrons and their activation potentials. These thinking machines have their beginnings in post WW-II research at MIT, in the work of Seymour Papert who introduced “Perceptrons,” and Norbert Weiner’s “Cybernetics”.

But do we know why there is sudden interest in these biologically inspired computer models ? It is due to GPUs which has accelerated all the complex computations associated with neural networks for it be practical in such a large scale. They allow these networks to operate on gigabytes (or even terabytes) of data and have significantly reduced the computation time from months to days, or days to hours, or hours to minutes usually by an order of magnitude – not possible in an earlier generation of computing. Before we jump into the details let us understand why we need deep learning and convolutional neural networks in the first place.

Scientific Innovations

Science and engineering have traditionally advanced by our ability to understand phenomena in natural world and describe them mathematically, since the times of Leonardo Da Vinci, Nicolas Copernicus, Galileo Galilei, Tycho Brahe, Johannes Kepler and Isaac Newton. However gaining models through experimentation and scientific breakthroughs piece-meal for each problem at hand is a slow process. Outside of Physics and Mathematics the scientific method is largely driven by an empirical approach.

It is in such pursuits of building models of unknown processes where observational data far exceed our human intelligence to divine an analytical model, the advent of deep learning and GPU based multi-layered neural networks provide an ad-hoc computable model. System identification for particular classification tasks, image recognition, and speech recognition to the modern miracle of a self-driving cars are all enabled by deep learning technology. All this came about due to the seminal work of many innovators culminating in the discovery of efficient convolutional neural networks by Prof. Geoff Hinton, who trained them by hardware acceleration via GPUs.

An original pioneer in the field of AI, before the AI winter, Prof. Geoff Hinton and co-workers [2] recently showed deep learning models that beat status-quo benchmarks on classification and prediction tasks on the following speech, text or image datasets: Reuters, TIMIT, MNIST, CIFAR and ImageNet, setting off the renewed interest in the field of AI from academia and industry giants – Google, Microsoft, Baidu and Facebook alike [3].

What is a GPU ?

GPU stands for Graphics Processing Unit [4]. These were originally designed for graphics rendering used in video games in 1990s. They have a large number of parallel cores which are very efficient for doing simple mathematical computations like matrix multiplications. These computations are the fundamental basis for machine learning methods such as deep learning. While the improvement in CPUs over years has slowed down over the years as Moore’s law has hit a bottleneck, the GPUs increase in performance has continued unabated showing tremendous improvements over the generations.

Figure. 1 (left): Deep Learning training task times as function of various GPU processors from NVidia. Figure. 2(right): AlexNet training throughput for 20 iterations on various CPU/GPU processing platforms.

Such GPUs were originally invented for shading algorithms algorithms, are now applied in training large machine learning models using a Open CL or CUDA like frameworks (variants of C-language with description for parallel execution via threading) from the vendors.

The pioneering hardware vendors include Nvidia with their GPU series like GeForce, Tesla; AMD with its Radeon, GP GPU, Google has entered this race with its TPU (Tensor Processing Unit) and some offerings from Intel for ML training applications. Nvidia and AMD are the main players in the GPU space with Nvidia laying special emphasis on parallel computing and deep learning over the years. Nvidia just announced the new Volta generation chip based GPU V100 which is about 2.5 x faster than the previous generation chip Pascal GP100 which was announced less than 2 years ago [5].Compared to CPU, however GPUs are more than 50x faster for Deep learning. Performance of GPUs as function of various GPU families in shown in Figure. 1, and for another AlexNet data set is shown in Figure. 2.

Hardware Innovation

If the Harvard architecture and RISC architecture based CPUs have been workhorses of personal computer revolution, then the advent of high framerate video-gaming pushed the CPU based graphics rendering from CPU + Video card based rendering to CPU + GPU, to CPU + GPU + GP-GPU (general purpose GPU); some of this overview is shown in Figure. 3a, 3b.

Figure. 3(a,b): Evolution of GPU performance from video graphics cards and rendering from CPU; courtesy PC Magazine [4]; Figure. 3(c): NVIDIA Tesla GPU applications in scientific research.


GPU’s are suitable for large numerical algorithms where various data have to be moved through a computational pipeline often in parallel; this SIMD problem, like genome sequencing shown in Figure. 3c, when solved by GPU gain the maximum speedup/acceleration. However, there is a fundamental limitations of GPU acceleration due to the Amdahl’s law which saturates the parallelization upto the available serial bottlenecks for a given computational task.

Software Frameworks

To build a deep learning application one may use their labeled datasets to build a learning model on any of the various frameworks [6] (both open-source or closed) provided from competing vendors in the industry as follows:

  1. TensorFlow, developed by google, python API over C++ engine, low level api, good for researchers, not commercially supported; notably Google is in process of developing a TPU – an advanced version of GPU for direct use with TensorFlow.

  2. Caffe 2, developed by UC Berkeley used at Facebook among other places, focussed on computer vision, one of the earlier frameworks to gain significant adoption, Python API over C++ and CUDA code

  3. Scikit Learn (Python based) general inference and machine-learning framework

  4. Theano written in python, grand-daddy of deep learning frameworks

  5. CNTK developed by Microsoft


Tamil applications for deep learning including providing or improving existing solutions to the problems of,

  1. Tamil Speech Recognition
  2. Tamil Character Recognition [7,8]
  3. Natural Language Processing for Tamil

Hardware acceleration and availability of big-data (labeled datasets) will play key role in the success of applying deep learning techniques to these problems.


  1. Jensen Huang, “Accelerating AI with GPUs: A New Computing Model,” link

  2. G. E. Hinton et-al. “Imagenet classification with deep convolutional neural networks,” Advances in Neural Information Processing Systems (2012).

  3. LeCun, Y., Bengio, Y. and Hinton, G. E., “Deep Learning” Nature, Vol. 521, pp 436-444. (2015), link.

  4. GPU definition at PC Magazine Encyclopedia, PC Magazine, (2017) link.

  5. Tesla GPU Application notes from NVidia, (2017) link.

  6. Comparing deep learning frameworks”, (2017), link.

  7. Prashanth Vijayaraghavan, Mishra Sra, “Handwritten Tamil Recognition using a Convolutional Neural Network,” NEML Poster (2015) link.

  8. R. Jagadeesh Kannan, S. Subramanian, “An Adaptive Approach of Tamil Character Recognition Using Deep Learning with Big Data-A Survey”, Proceedings of 49th Annual Convention of Computer Society of India (vol. 1) pp 557-567 (2015), link.

Ezhil, Open-Tamil conference articles – 2017

One of major achievements of last year has been collecting inputs from our team and writing up two important papers – one for historical review and other for collective call to action on great opportunity that is Tamil open-source software.


We also take time to thank all co-authors who have pulled together their efforts at short notice to make these research works happen! Together these two papers represent a value of tens of thousands of Indian rupees, or more in the making (going by estimates of other Tamil software foundations).

We also thank conference organizers for partial travel grant toward making this presentation happen. Thank you!

Conference Articles – 2017

Ezhil, Open-Tamil conference articles – 2017 presented at Tamil Internet Conference, August, 2017, in Toronto, Canada. Both the papers were well received and good academic and development points were debated at the forum.

  1. Ezhil – எழில் மொழி பொது பயன்பாட்டிற்கும், வெளியீடு நோக்கிய சவால்களும்
    • This paper summarizes the path taken by Ezhil from inception toward delivering a fully installable product on Windows 64/32bit, Linux (Unbutnu, Fedora) systems, and offers a meditation on how students and teachers may adopt this product, and future pathways.
    • Presentation slides are here on slideshare.
  2. Open-Tamil / Open Source in Tamil – Tamil Open-Source Landscape – Opportunities and Challenges_MA_2017_final 
    • Some important contributions of this paper show collective interest in Tamil open-source which outpaces the other languages with larger speaking-population. This is a key indicator to develop better pathways to bring new developers and train them into developing Tamil software
    • GitHub Tamil language repositories

      GitHub Tamil language repositories compared with other languages, as measure of software developers interest.

    • Presentation slides are at slide-share

For questions and queries on these articles, please write to us at or leave your comments below.

Ezhil Language Foundation


alpha release – எழில் 0.9 முன்-பரிசோதனை திரட்டி

எழில் முன் பரிசோதனை திரட்டி windows மற்றும் linux-க்கு இங்கு பெறலாம். கடினமாக உழைத்த குழுவினருக்கு நன்றி.

இதில் நீங்கள் பெற கூடிய செயலிகள்,

  1. எழில் இயக்கி “ezhili” (terminal – முனையம் இடைமுகம்)
  2. எழில் திருத்தி “ezhuthi” (GUI – பயனர் திரை இடைமுகம்)
  3. தமிழில் நிரல் எழுது புத்தகம்

உங்களுக்கு எதுவும் தடங்கல், பிழை செய்திகள், விருப்ப தேவைகள் இருந்தால் எல்லா அன்பாயும் மின்னஞ்சலில்இக்கு அனுப்பவும். திட்டினால் படிக்கவேமாட்டோம்.


எழில் குழு


Open-Tamil and Ezhil updates (2016)

Today we are releasing updates to two packages maintained by Ezhil Language Foundation;

  1. open-tamil v0.65
    • open-tamil package contains minor bug fixes and solid performance on Python 2 and Python 3.
    • pip install –upgrade open-tamil
  2. ezhil-v0.82
    • Fix some issues for Python 3 installation from the previous release
    • pip install –upgrade ezhil

Both these packages maybe downloaded from PyPi (Python Package Index) via ‘pip’ command.

Thanks very much to the original contributors, bug reporters, and Tamil open-source software (TOSS) enthusiasts.


Tamil Text to Speech Synthesizers – Topical Overview

There are several open-source Tamil Text to Speech Synthesizers (TTS) available to date. Google uses one of these behind the scenes; we may wonder which one. Some of these have previously been reviewed by others at Azhagi and elsewhere on Tamil interwebs.

  1. eSpeak (Rule based synthesizer), GPL licensed (technique formant LPC analysis based)
  2. Festival  (Univ of Endinbrugh)(Tamil voice added on Feb, 2015), BSD like license.
    1. Research carried out by IISc team lead by Prof. A.G. Ramakrishnan
  3. tamil-tts by Prof. Vasu Renganathan, GPL (technique unit selection based)
  4. Android-tts – hack the English TTS to speak Tamil by transliteration
    1. This is a hack suggested by yours truly;
    2. e.g. to speak the phrase, “சும்மா இருக்கியா?” we use the English TTS via the transliteration, “Summaa Irukkiyaa?”. Clearly this maybe sub-optimal but work as a hack.

Opinion : While there are purpoted speech synthesizers in Tamil from academic government (tax-payer) funded projects like SSN-IIT collaboration, they are not publicly available or easily licensable. It is a situation that needs to be remedied.

Linguistic Aspects: One of key issues that make Tamil TTS a relatively easy goal is because the front-end is easily achieved due to the phoneme-orthography of Tamil language – i.e. Tamil language is itself a “phonetic language” where written spelling and phonetic/spoken forms are identical. This is not so in case of  English and European languages like French where there are silent words, and exceptions to rules almost all the time. Tamil language has few exceptions if any.

The back-end of the TTS engine is usually formed by LPC analysis or other source-filter separation algorithms which is an exciting and continually engaging problem for Signal processing engineers, computer scientists, and computational linguists.

Further research for Tamil TTS should improve on the naturalness and ability to include these wonderful tools of previous generation of engineers into our applications for mobile and desktop devices. Google scholar is your friend – start here!.

Tamil morphological analyzer efforts on Github

Today after a long workday, and running 2miles to stay healthy, I checked out some of latest Tamil happenings on Github. Indeed there are 43 paginated results for search for ‘Tamil’ projects – about 430 projects to do with Tamil.

Anyway – the rare find it is – I was very pleasantly surprised by the ongoing effort of Pranavan Thevendiran and team to Develop a Tamil Morphological Analyzer: at

This is also along the lines of a expertly developed Java project by VelSubra at

Congratulations on a great start, and greetings from another language aficionado.

Vazhthukkal, nanbargale!