We are a small team of young people, with different backgrounds, but same passion for solving problems in new ways.
We take inspiration from the world around us, how it works, interacts and evolves to fuel our ideas and development no matter if they are computer vision algorithms for object detection and recognition, or data analytics.
We've been interested in multicore processors since 2009, and recently we have been evaluating the Kalray MPPA-256 Manycore platform for high-performance computing, computer vision as well as 4K HEVC video encoding.
Computer Vision is one of our main areas of research nowadays, mainly focusing on vehicle traffic analysis and computing different properties.
Taking inspiration from nature to solve practical problems, we are focusing our research on running Neural networks on Parallel/Multicore CPUs
Evaluating performance of Kalray 256 and 1024 core systems on huge datasets
Custom software development for DELL Statistica Enterprise for big data applications.
A client-server system, monitoring in real time the staff entry/edit data performance
We are researching different computer vision algorithms (CVA) applicable in the areas of traffic analysis.
Currently we are focused on finding out stable methods for feature extraction specific to the traffic analysis domain.
Technologies: C++, OpenCV, Kalray MPPA-256
A thesis for Stefan Chahanov's high school graduation.
The system combines automatic image acquisition, clustering and classification, learning and producing results.
The basic idea is that we have a picture, and we ask the system if there is a specific object in that picture. The system may or may not have prior knowledge of what that object is. If it doesn't know it, it searches for information on the Internet and tries to learn what that object is and then detect it.
As an output the system gave predictions in the form of images, what it considered the requested object was.
Technologies: Java, TileEncore-Gx 36 multicore board, Linux.
A diploma thesis for Iliyan Gochev's BS.C. degree in applied mathematics, this project was our first dive into the world of multicore CPUs.
The main goal of the project was to try out and evaluate Tilera's TilePro architecture for executing artificial neural networks (ANNs).
Two different tasks were selected for the evaluation: the first was for optical character recognition (20,000 records, 18 variables used in the model building), the second was predicting credit default (1000 records, 15 variables used in the model building). It was both educational on the platform specifics, as well as the theory of ANNs. The diploma thesis was considered as innovative by the board of examiners at the Technical University - Sofia.
The source was written in C++, which allowed for easy port to the Kalray MPPA-256 architecture recently and evaluating the platforms side by side.
Future investigations include using Kalray's SigmaC for developing more natural neural data processing.
Technologies: C++, TilePro 64 core CPU in 1U server, Linux.