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Deep Learning / Machine-Learning Hardware / Neural Networks / Biomedical Instrumentation / Integrated Circuits
We work on Deep Learning software and hardware. Our goal is to replicate the human brain in algorithms and computing devices. Our focus is on the creation of an artificial scientist using multi-modal large world models (LWM). #LLM #AI #DeepLearning #MachineLearning
Research questions: How do we teach machines to understand and predict the 3D world we live in? How do we encode and learn multi-modal data? What artificial brain can support intelligent behavior in a physical environment? What hardware can we use to accelerate machine learning?
Research philosophy: We specialize in the use of computing technologies to extend scientific exploratory methods and measurement tools for understanding life and replicating it in engineered systems.
Team: Eugenio Culurciello, Sravani Ramishetty, Shakti Wadekar, Viktor Ciroski
Deep Learning Software And Algorithms
We are pioneers in deep learning and neural networks, with more than 20 years of experience across vision, speech, text, robotics, 3D and graphics.
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Deep Learning Processors And Accelerators
We work on hardware processors and accelerators for deep learning, including multiple generations of processors and accelerators from 2004-present.
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Micro-Chips
We worked on silicon devices, circuits and systems for biomedical applications, neuromorphic engineering, silicon-on-insulator and silicon-on-sapphire.
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Scaling Up Machine Learning
Chapter: Large-Scale FPGA-Based Convolutional Networks, Cambridge University Press, 2011.
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Silicon-on-Sapphire Circuits And Systems
Sensor and biosensor interfaces, E. Culurciello, McGraw Hill, 2009.
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