Enigma Lab
Overview
Significant advancements are needed in the development of new class of AI architectures with emerging devices and algorithms that take advantage of distributed networks with sparse activity, taking inspiration from biology, neuroscience and machine learning.
At the ENIGMA lab, we build event-based models and architectures that are inspired by the sensory and neural systems of the human body. We strive to create energy-efficient Neuromorphic systems, spanning from devices to applications, by integrating cutting-edge event-based sensors and neural network architectures, along with scalable and efficient algorithms (spike-based or event-based), for seamless operation and real-time decision making, with low latencies and high adaptability to variations. Though the current thrust is on Visual Perception using Dynamic Vision Sensors (DVS) and Compute-In-Memory VLSI hardware, we plan to expand to other sensory modalities including auditory and tactile (touch) sensors. The long term goal is multi-sensory integration and processing leading to Neuromorphic Intelligence.
Latest News
IMPORTANT! We are currently looking to hire 1 PhD student with exceptional skills to work on analog and digital integrated circuit (IC) design. Prior experience with Cadence, and Synopsys tools and tapeout experience is preferred.
We won a major NSF FuSe (Future of Semiconductors) grant! Our story appeared on the Pitt-ECE website here.
Kangni and Shahin have a paper on BioNN accepted at IEEE JETCAS Journal! Check our paper here.
Our work on Compute-In-Memory hardware using Resistive Random Access Memory (RRAM) was published in Nature recently!
Interested in joining the Enigma Lab?
For both undergraduate and graduate students interested in neuromorphic systems research and have a passion for analog and digital circuit design, computer vision, algorithms for machine learning, please email Dr. Rajkumar Kubendran (rajkumar.ece@pitt.edu) with your CV.
IMPORTANT! We are currently looking to hire 1 PhD student with exceptional skills to work on analog and digital integrated circuit (IC) design. Prior experience with Cadence, and Synopsys tools and tapeout experience is preferred.
Helpful Experience
- Hardware Design for CMOS cameras or other sensors
- VLSI Circuit Design and Computer Architecture
- CMOS Chip Tape-Out and Testing
- Good understanding of Machine Learning and Deep Neural Networks
- Strong coding abilities (C++ and Python)
- Deep Learning platforms (PyTorch or Tensorflow)