Artificial Intelligence has been assuming significance beyond academic debates in the past couple of years. Google and Facebook claims that they now have face recognition systems based on AI that can beat humans at the task.

Human Brain vs Artificial Intelligence Systems

Artificial Intelligence has been assuming significance beyond academic debates in the past couple of years. Google and Facebook have claimed that they now have face recognition systems based on Artificial Intelligence that can beat humans at the task. There are reports that many of the text chats are now manned by Artificial Intelligence systems without the user’s knowledge, thus surpassing the Turing test criterion.  Proponents of Artificial Intelligence like Ray Kurzweil has been predicting that within the next 30 years, AI will enable immortality through a concept known as Singularity, where we will be able to upload our brain on to a cloud and then onwards, our thoughts live on forever. On the other end of the spectrum, people like Stephen Hawking predict Artificial Intelligence could spell the end of human civilization with computer systems eventually overpowering humans.  So, it would be interesting to explore objectively where a computer artificial intelligence system stands vis-à-vis a human brain. One of the biggest advantages a computer system has over a human is the speed of processing. A 1GHz processor can perform a single operation in 1 nanosecond. Even assuming that it takes about 40 CPU cycles to transfer the data from memory before processing, a computer can do a single operation including data fetch in about 40 nanoseconds. Compare this to the human neuron which collects inputs from a synapse, processes it and transfers it to the next neuron in about 5 milliseconds. This would mean that a computer system is 125,000  times faster than the human neuron. Yet, if you show 100 different objects to a human and ask him to identify each within five seconds and repeat the same exercise with a computer, a human would fare far better. To answer why there is such anomaly, we will have to go into the detailed functioning of a single task, say object classification in computers vis-à-vis a human. The two systems start differing from the sensor node itself. Human visual sensory nodes are rods and cones for detecting light and color respectively. The 100 million rods and 6 million cones convert the data contained in the light to sensory impulses and transfer them to the neurons in parallel. However, a computer system is more or less a serial system. After the light is converted to electrical signals in the camera, they are scanned and sent serially. Even at a lane rate of 5.7 Gbps along four lanes, we could transmit about 170 UHD frames in a second. This is still much faster than the retentivity of the eye. So the problem lies elsewhere. The basic difference between a human system and a computer system is that the human neural system takes much lesser number of steps to convert the visual stimulus to an inference when compared to a computer system. If a single neuron is to take about 5 milliseconds to process the data and transfer to the next neuron, the whole cycle of inference should take within 100 neurons if we were to make a correct inference in 500 milliseconds. Compare this is to a computer vision system. Each image is subject to the broad steps of image pre-processing, edge detection, feature extraction and object recognition. For image pre-processing and edge detection, one pixel at a time is processed. Each pixel is subject to at least 10 to 15 computations, while feature extraction and object recognition could be performed using either conventional image recognition systems using statistical methods or using neural networks with multiple hidden layers. In any case, the number of computations required to reach an inference is easily in the order of millions of CPU cycles.   As the number of objects increase, the number of hidden neuron layers also increase, thus making such a system much slower than a human.  The current parallel architecture offered by GPUs has some advantage in making the process faster. However, the sheer number of computations required to reach an inference coupled with the need for these computational stages to interact with each other makes an AI system look primitive when compared to the human brain, even though it has a clear advantage of speed. So why do neurons require lesser stages of processing? What are the additional capabilities they possess when compared to a computer system? The answers are not clear, but there are some indications:
  1. Aggregation: – The human inference system does not work on a step-by-step method of looking at one pixel at a time, pre-processing it, detecting an edge, then detecting a higher-order feature and then doing an object recognition/classification. Many of these steps are aggregated. For example, if we see from a distance, a rose plant with red roses on it, even if the details of the flower are not visible, we are able to infer that it is a rose. There are some who argue that this is because the human system is a look-up based system where the image of a rose is compared to what is present in memory without many steps of intermediate processing so that we finally arrive at the inference much faster. However, this view is an oversimplification. If our frame of view has multiple objects, we are able to segregate every object and separately classify each one at the same time. So segregation is still a function done by the human brain just like edge detection. The aggregation is done dynamically, i.e., sometimes our attention zooms into a bee sitting on the flower and sometimes we totally miss it even from the same distance of view. We are able to focus on different objects of interest even while aggregating, which is a capability that has not evolved in computer systems.
  2. Association: – Consider a person goes to Japan during cherry blossom season. He has never seen a cherry tree let alone its flower his whole life. However, if he sees a lot of trees in full bloom, the very knowledge that it is cherry blossom season will make him associate the tree to a cherry tree. However, computers are still bad at this. The deep learning system has to be trained with a labeled object of each item it has to classify. If it is not trained with a cherry tree, it would not figure out the identity through a priori knowhow of the season and country.
  3. Filtering Noise: – Let us suppose we are given a drawing that is scribbled all over by a child with different sketch pens. The human brain is still capable of filtering the noise and inferring the image on the paper. Image processing systems use methods like smoothing to remove Gaussian noise and morphological operations to eliminate noise with a pattern. But if there is intentional noise which has similar characteristics of the image itself, like a child’s scribble, image processing systems fail to eliminate this. The auditory ability of filtering noise is even more amazing. Even in a highly noisy environment where the ambient noise is several decibels higher, we are still able to pick up what our friend is speaking to us. There are filters developed based on frequencies in audio systems that mask out ambient noise. But the unique ability of human sensory function is that we can dynamically focus on one voice even if there are many human voices in the background.
  4. Abstraction: – There are amazing levels of abstraction that the human mind can work with. Let us, again, take the rose example. We do not think so much about the number of petals of the rose, but aggregate it to a single red rose. A computer vision system also strives to bring in some levels of abstraction by removing high degrees of correlation. This is done by computing the principal components or Eigen values of a picture. But the very process of computing Eigen values takes close to a millisecond in modern GPUs. Also, this is more of a reductionist technique rather than an abstraction technique in order to make statistical correlation with the required object simpler. However, the human mind does not stop here. We associate adjectives like “beautiful” to the rose. We could then go off on a higher order trajectory of language and think of the Shakespearean verse, “A rose by any other name would smell as sweet.” We could associate feelings of love with the rose, and so on and so forth. An idle mind is indeed a devil’s workshop. In fact, the progress of the human race owes heavily to our ability to abstract. We create simpler models to explain very complex systems and then work with these models. A physicist who works on matter and energy hands over the system to a chemist once we start looking at a molecular level. The chemist in turn hands it over to a biologist when we begin looking at genes, cells and simple organisms. A biologist passes the baton to a doctor when the system grows in size to form a human system like the nervous system. They then give it to a psychologist when the abstraction level becomes as large as the mind. Then comes the layers of philosophy, political science, economics, arts, belief systems, et-al. And amazingly, the same human being can use these different levels of abstraction as the situation demands. Da Vinci was a scientist and a painter at the same time.
  5. Rapid reduction of the need for focused attention through training: – All of us remember the first time we learned to cycle. It was virtually impossible to keep our center of gravity below the wheels of the cycle to keep from falling. Even after struggling through this first step, when we had to turn a corner or take some higher order decision, like braking, we would lose our balance and fall. However, gradually all these tasks became a no- brainer. After a few months of cycling, the cyclist’s mind is preoccupied with unrelated thoughts most of the time and the only time he would focus back on the act of cycling is when he has to take a higher order decision like braking. However, computer systems take the same amount of resources to perform a task, no matter how long they are trained. Deep learning systems become better and better over time and reduce the output error. However, the resources taken remain the same and therefore, they are not freed up for some other task. In fact, the human system has a huge number of completely autonomic tasks which we do without cognition. Our heart pumps blood many times a second, our digestive system processes food and our respiratory system takes in oxygen and gives out carbon dioxide, none of which is with our active cognition. Computer systems have such partners like DMA engines, which are used to offload the CPU. However, all of them require regular housekeeping by the CPU.
  6. Handling diverse sensory functions and correlating inferences from them in parallel:- The human brain can see a rose, smell it, feel its soft petals, hear a bee buzzing around it while chewing a candy. Computers can also independently do multiple functions, but correlating all of them to stimulate higher-order feelings like happiness still has not evolved. There are algorithms that perform sensor fusion, but these are premature compared to human capability.
The mechanisms of how the human brain achieves many of the above are still unknown. Key research is still ongoing to understand the human brain better and emulate these in computer systems. Until such research matures, we might not achieve what we can classify as a truly artificial intelligent system. Individual functions like auto-chat may pass the Turing test criteria. But the human brain is such a wonderfully complex system that emulating all these functions together may be a couple of generations away. Or so let us hope, unless we want computer systems to overpower humans and cause our decline while we are still alive.

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Human Pose Detection & Classification

Some Buildings in a city


  • Suitable for real time detection on edge devices
  • Detects human pose / key points and recognizes movement / behavior
  • Light weight deep learning models with good accuracy and performance

Target Markets:

  • Patient Monitoring in Hospitals
  • Surveillance
  • Sports/Exercise Pose Estimation
  • Retail Analytics

OCR / Pattern Recognition

Some Buildings in a city

Use cases :

  • Analog dial reading
  • Digital meter reading
  • Label recognition
  • Document OCR

Highlights :

  • Configurable for text or pattern recognition
  • Simultaneous Analog and Digital Dial reading
  • Lightweight implementation

Behavior Monitoring

Some Buildings in a city

Use cases :

  • Fall Detection
  • Social Distancing

Highlights :

  • Can define region of interest to monitor
  • Multi-subject monitoring
  • Multi-camera monitoring
  • Alarm triggers

Attire & PPE Detection

Some Buildings in a city

Use cases :

  • PPE Checks
  • Disallowed attire checks

Use cases :

  • Non-intrusive adherence checks
  • Customizable attire checks
  • Post-deployment trainable


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    Real Time Color Detection​

    Use cases :

    • Machine vision applications such as color sorter or food defect detection

    Highlights :

    • Color detection algorithm with real time performance
    • Detects as close to human vison as possible including color shade discrimination
    • GPGPU based algorithm on NVIDIA CUDA and Snapdragon Adreno GPU
    • Extremely low latency (a few 10s of milliseconds) for detection
    • Portable onto different hardware platforms

    Missing Artifact Detection

    Use cases :

    • Detection of missing components during various stages of manufacturing of industrial parts
    • Examples include : missing nuts and bolts, missing ridges, missing grooves on plastic and metal blocks

    Highlights :

    • Custom neural network and algorithms to achieve high accuracy and inference speed
    • Single-pass detection of many categories of missing artifacts
    • In-field trainable neural networks with dynamic addition of new artifact categories
    • Implementation using low cost cameras and not expensive machine-vision cameras
    • Learning via the use of minimal training sets
    • Options to implement the neural network on GPU or CPU based systems

    Real Time Manufacturing Line Inspection

    Use cases :

    • Detection of defects on the surface of manufactured goods (metal, plastic, glass, food, etc.)
    • Can be integrated into the overall automated QA infrastructure on an assembly line.

    Highlights :

    • Custom neural network and algorithms to achieve high accuracy and inference speed
    • Use of consumer or industrial grade cameras
    • Requires only a few hundred images during the training phase
    • Supports incremental training of the neural network with data augmentation
    • Allows implementation on low cost GPU or CPU based platforms

    Ground Based Infrastructure analytics

    Some Buildings in a city

    Use cases :

    • Rail tracks (public transport, mining, etc.)
    • Highways
    • Tunnels

    Highlights :

    • Analysis of video and images from 2D & 3D RGB camera sensors
    • Multi sensor support (X-ray, thermal, radar, etc.)
    • Detection of anomalies in peripheral areas of core infrastructure (Ex: vegetation or stones near rail tracks)

    Aerial Analytics

    Use cases :

    • Rail track defect detection
    • Tower defect detection: Structural analysis of Power
      transmission towers
    • infrastructure mapping

    Highlights :

    • Defect detection from a distance
    • Non-intrusive
    • Automatic video capture with perfectly centered ROI
    • No manual intervention is required by a pilot for
      camera positioning


    Co-founder & CEO


    Founder and Managing director of Ignitarium, Sanjay has been responsible for defining Ignitarium’s core values, which encompass the organisation’s approach towards clients, partners, and all internal stakeholders, and in establishing an innovation and value-driven organisational culture.


    Prior to founding Ignitarium in 2012, Sanjay spent the initial 22 years of his career with the VLSI and Systems Business unit at Wipro Technologies. In his formative years, Sanjay worked in diverse engineering roles in Electronic hardware design, ASIC design, and custom library development. Sanjay later handled a flagship – multi-million dollar, 600-engineer strong – Semiconductor & Embedded account owning complete Delivery and Business responsibility.


    Sanjay graduated in Electronics and Communication Engineering from College of Engineering, Trivandrum, and has a Postgraduate degree in Microelectronics from BITS Pilani.


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      RAMESH EMANI Board Member


      Board Member

      Ramesh was the Founder and CEO of Insta Health Solutions, a software products company focused on providing complete hospital and clinic management solutions for hospitals and clinics in India, the Middle East, Southeast Asia, and Africa. He raised Series A funds from Inventus Capital and then subsequently sold the company to Practo Technologies, India. Post-sale, he held the role of SVP and Head of the Insta BU for 4 years. He has now retired from full-time employment and is working as a consultant and board member.


      Prior to Insta, Ramesh had a 25-year-long career at Wipro Technologies where he was the President of the $1B Telecom and Product Engineering Solutions business heading a team of 19,000 people with a truly global operations footprint. Among his other key roles at Wipro, he was a member of Wipro's Corporate Executive Council and was Chief Technology Officer.


      Ramesh is also an Independent Board Member of eMIDs Technologies, a $100M IT services company focused on the healthcare vertical with market presence in the US and India.


      Ramesh holds an M-Tech in Computer Science from IIT-Kanpur.


      General Manager - Marketing

      A professional with a 14-year track record in technology marketing, Malavika heads marketing in Ignitarium. Responsible for all branding, positioning and promotional initiatives in the company, she has collaborated with technical and business teams to further strengthen Ignitarium's positioning as a key E R&D services player in the ecosystem.

      Prior to Ignitarium, Malavika has worked in with multiple global tech startups and IT consulting companies as a marketing consultant. Earlier, she headed marketing for the Semiconductor & Systems BU at Wipro Technologies and worked at IBM in their application software division.

      Malavika completed her MBA in Marketing from SCMHRD, Pune, and holds a B.E. degree in Telecommunications from RVCE, Bengaluru.



      VP - Operations

      Pradeep comes with an overall experience of 26 years across IT services and Academia. In his previous role at Virtusa, he played the role of Delivery Leader for the Middle East geography. He has handled complex delivery projects including the transition of large engagements, account management, and setting up new delivery centers.

      Pradeep graduated in Industrial Engineering and Management, went on to secure an MBA from CUSAT, and cleared UGN Net in Management. He also had teaching stints at his alma mater, CUSAT, and other management institutes like DCSMAT. A certified P3O (Portfolio, Program & Project Management) from the Office of Government Commerce, UK, Pradeep has been recognized for key contributions in the Management domain, at his previous organizations, Wipro & Virtusa.

      In his role as the Head of Operations at Ignitarium, Pradeep leads and manages operational functions such as Resource Management, Procurement, Facilities, IT Infrastructure, and Program Management office.


      SONA MATHEW Director – Human Resources


      AVP – Human Resources

      Sona heads Human Resource functions - Employee Engagement, HR Operations and Learning & Development – at Ignitarium. Her expertise include deep and broad experience in strategic people initiatives, performance management, talent transformation, talent acquisition, people engagement & compliance in the Information Technology & Services industry.


      Prior to Ignitarium, Sona has had held diverse HR responsibilities at Litmus7, Cognizant and Wipro.


      Sona graduated in Commerce from St. Xaviers College and did her MBA in HR from PSG College of Technology.



      Vice President - Sales

      As VP of Sales, Ashwin is responsible for Ignitarium’s go-to-market strategy, business, client relationships, and customer success in the Americas. He brings in over a couple of decades of experience, mainly in the product engineering space with customers from a wide spectrum of industries, especially in the Hi-Tech/semiconductor and telecom verticals.


      Ashwin has worked with the likes of Wipro, GlobalLogic, and Mastek, wherein unconventional and creative business models were used to bring in non-linear revenue. He has strategically diversified, de-risked, and grown his portfolios during his sales career.


      Ashwin strongly believes in the customer-first approach and works to add value and enhance the experiences of our customers.


      AZIF SALY Director – Sales


      Vice President – Sales & Business Development

      Azif is responsible for go-to-market strategy, business development and sales at Ignitarium. Azif has over 14 years of cross-functional experience in the semiconductor product & service spaces and has held senior positions in global client management, strategic account management and business development. An IIM-K alumnus, he has been associated with Wipro, Nokia and Sankalp in the past.


      Azif handled key accounts and sales process initiatives at Sankalp Semiconductors. Azif has pursued entrepreneurial interests in the past and was associated with multiple start-ups in various executive roles. His start-up was successful in raising seed funds from Nokia, India. During his tenure at Nokia, he played a key role in driving product evangelism and customer success functions for the multimedia division.


      At Wipro, he was involved in customer engagement with global customers in APAC and US.


      RAJU KUNNATH Vice President – Enterprise & Mobility


      Distinguished Engineer – Digital

      At Ignitarium, Raju's charter is to architect world class Digital solutions at the confluence of Edge, Cloud and Analytics. Raju has over 25 years of experience in the field of Telecom, Mobility and Cloud. Prior to Ignitarium, he worked at Nokia India Pvt. Ltd. and Sasken Communication Technologies in various leadership positions and was responsible for the delivery of various developer platforms and products.


      Raju graduated in Electronics Engineering from Model Engineering College, Cochin and has an Executive Post Graduate Program (EPGP) in Strategy and Finance from IIM Kozhikode.


      PRADEEP SUKUMARAN Vice President – Business Strategy & Marketing


      Vice President - Software Engineering

      Pradeep heads the Software Engineering division, with a charter to build and grow a world-beating delivery team. He is responsible for all the software functions, which includes embedded & automotive software, multimedia, and AI & Digital services

      At Ignitarium, he was previously part of the sales and marketing team with a special focus on generating a sales pipeline for Vision Intelligence products and services, working with worldwide field sales & partner ecosystems in the U.S  Europe, and APAC.

      Prior to joining Ignitarium in 2017, Pradeep was Senior Solutions Architect at Open-Silicon, an ASIC design house. At Open-Silicon, where he spent a good five years, Pradeep was responsible for Front-end, FPGA, and embedded SW business development, marketing & technical sales and also drove the IoT R&D roadmap. Pradeep started his professional career in 2000 at Sasken, where he worked for 11 years, primarily as an embedded multimedia expert, and then went on to lead the Multimedia software IP team.

      Pradeep is a graduate in Electronics & Communication from RVCE, Bangalore.


      SUJEET SREENIVASAN Vice President – Embedded


      Vice President – Automotive Technology


      Sujeet is responsible for driving innovation in Automotive software, identifying Automotive technology trends and advancements, evaluating their potential impact, and development of solutions to meet the needs of our Automotive customers.

      At Ignitarium, he was previously responsible for the growth and P&L of the Embedded Business unit focusing on Multimedia, Automotive, and Platform software.

      Prior to joining Ignitarium in 2016, Sujeet has had a career spanning more than 16 years at Wipro. During this stint, he has played diverse roles from Solution Architect to Presales Lead covering various domains. His technical expertise lies in the areas of Telecom, Embedded Systems, Wireless, Networking, SoC modeling, and Automotive. He has been honored as a Distinguished Member of the Technical Staff at Wipro and has multiple patents granted in the areas of Networking and IoT Security.

      Sujeet holds a degree in Computer Science from Government Engineering College, Thrissur.


      RAJIN RAVIMONY Distinguished Engineer


      Distinguished Engineer


      At Ignitarium, Rajin plays the role of Distinguished Engineer for complex SoCs and systems. He's an expert in ARM-based designs having architected more than a dozen SoCs and played hands-on design roles in several tens more. His core areas of specialization include security and functional safety architecture (IEC61508 and ISO26262) of automotive systems, RTL implementation of math intensive signal processing blocks as well as design of video processing and related multimedia blocks.


      Prior to Ignitarium, Rajin worked at Wipro Technologies for 14 years where he held roles of architect and consultant for several VLSI designs in the automotive and consumer domains.


      Rajin holds an MS in Micro-electronics from BITS Pilani.


      SIBY ABRAHAM Executive Vice President, Strategy


      Executive Vice President, Strategy


      As EVP, of Strategy at Ignitarium, Siby anchors multiple functions spanning investor community relations, business growth, technology initiatives as well and operational excellence.


      Siby has over 31 years of experience in the semiconductor industry. In his last role at Wipro Technologies, he headed the Semiconductor Industry Practice Group where he was responsible for business growth and engineering delivery for all of Wipro’s semiconductor customers. Prior to that, he held a vast array of crucial roles at Wipro including Chief Technologist & Vice President, CTO Office, Global Delivery Head for Product Engineering Services, Business Head of Semiconductor & Consumer Electronics, and Head of Unified Competency Framework. He was instrumental in growing Wipro’s semiconductor business to over $100 million within 5 years and turning around its Consumer Electronics business in less than 2 years. In addition, he was the Engineering Manager for Enthink Inc., a semiconductor IP-focused subsidiary of Wipro. Prior to that, Siby was the Technical Lead for several of the most prestigious system engineering projects executed by Wipro R&D.


      Siby has held a host of deeply impactful positions, which included representing Wipro in various World Economic Forum working groups on Industrial IOT and as a member of IEEE’s IOT Steering Committee.


      He completed his MTech. in Electrical Engineering (Information and Control) from IIT, Kanpur and his BTech. from NIT, Calicut


      SUJEETH JOSEPH Chief Product Officer


      Chief Technology Officer


      As CTO, Sujeeth is responsible for defining the technology roadmap, driving IP & solution development, and transitioning these technology components into practically deployable product engineering use cases.


      With a career spanning over 30+ years, Sujeeth Joseph is a semiconductor industry veteran in the SoC, System and Product architecture space. At SanDisk India, he was Director of Architecture for the USD $2B Removable Products Group. Simultaneously, he also headed the SanDisk India Patenting function, the Retail Competitive Analysis Group and drove academic research programs with premier Indian academic Institutes. Prior to SanDisk, he was Chief Architect of the Semiconductor & Systems BU (SnS) of Wipro Technologies. Over a 19-year career at Wipro, he has played hands-on and leadership roles across all phases of the ASIC and System design flow.


      He graduated in Electronics Engineering from Bombay University in 1991.


      SUJITH MATHEW IYPE Co-founder & CTO


      Co-founder & COO


      As Ignitarium's Co-founder and COO, Sujith is responsible for driving the operational efficiency and streamlining process across the organization. He is also responsible for the growth and P&L of the Semiconductor Business Unit.


      Apart from establishing a compelling story in VLSI, Sujith was responsible for Ignitarium's foray into nascent technology areas like AI, ML, Computer Vision, and IoT, nurturing them in our R&D Lab - "The Crucible".


      Prior to founding Ignitarium, Sujith played the role of a VLSI architect at Wipro Technologies for 13 years. In true hands-on mode, he has built ASICs and FPGAs for the Multimedia, Telecommunication, and Healthcare domains and has provided technical leadership for many flagship projects executed by Wipro.


      Sujith graduated from NIT - Calicut in the year 2000 in Electronics and Communications Engineering and thereafter he has successfully completed a one-year executive program in Business Management from IIM Calcutta.


      RAMESH SHANMUGHAM Co-founder & COO


      Co-founder & CRO

      As Co-founder and Chief Revenue Officer of Ignitarium, Ramesh has been responsible for global business and marketing as well as building trusted customer relationships upholding the company's core values.

      Ramesh has over 25 years of experience in the Semiconductor Industry covering all aspects of IC design. Prior to Ignitarium, Ramesh was a key member of the senior management team of the semiconductor division at Wipro Technologies. Ramesh has played key roles in Semiconductor Delivery and Pre-sales at a global level.

      Ramesh graduated in Electronics Engineering from Model Engineering College, Cochin, and has a Postgraduate degree in Microelectronics from BITS Pilani.