JUMP: Memory/Logic

Joint University Microelectronics Program (JUMP) Theme 4: Merged Logic and Memory

Applications and Systems driven Center for Energy-Efficient Integrated NanoTechnologies (ASCENT)


Cognitive technologies are poised to have significant impact on future autonomous learning machines. While deep neural networks have already enabled large-scale machine learning applications, today’s cognitive hardware suffer from long training times, large training datasets, lack of flexibility and dynamic adaptation, and real-time autonomous decision-making. In the Applications and Systems driven Center for Energy-Efficient Integrated NanoTechnologies  (ASCENT) we endeavor to harness the latest innovations in novel devices (such as multi-state FeRAM, 3D vertical RRAMs, analog RPUs, stochastic oxide and spin neurons) in conjunction with breakthroughs in architectures and computational models (such as processing-in-memory, hyper-dimensional, goal-directed learning, probabilistic computing) to demonstrate cognitive hardware that overcomes current limitations through dynamic adaptation and parallel on-line learning. Novel integration technologies will enable solving large real world problems in the capacity limited regime, while enabling ubiquitous intelligence in the energy constrained setting. To address privacy and security issues associated with ubiquitous machine intelligence, accelerators that operate on encrypted domains will be designed with transformative impact on cyber-security.


As a part of the ASCENT center, PIs from Georgia Tech are conducting breakthrough research to enable platform technologies and computing models that can accelerate the next-generation of electronics to an era of intelligent systems. We are working with our industrial partners to develop electronic devices based on novel physics that can enable unique, brain-inspired computing technologies. Circuit and system architectures that address the memory-bottleneck are pursued. This needs to be complemented with a benchmarking effort across various technology and design choices and Georgia Tech is leading the research in this area. Our collaborative team as well as joint research with other JUMP centers is enabling orders of magnitude improvement in energy-efficiency as well as performance of memory-centric, stochastic and neuro-inspired computing models.


 •Arijit Raychowdhury (Theme 4 Lead) Shimeng Yu •Asif Khan •Azad Naeeimi


 •Yandong Luo

Collaborating Universities

•University of Notredame •University of California, Berkeley  •University of California, Santa Barbara (UCSB) •Purdue University •University of California, Los Angeles (UCLA) •Stanford University 


Semiconductor Research Corporation •DARPA •Several member companies