The Center for Research into Novel Computing Hierarchies (CRNCH) has launched a new fellowship program to support innovative student research in post-Moore computing topics. The first three fellows are Ph.D. students Dingtian Zhang, Muliang Zhu, and Chunxing Yin.

The fellowship awards partial funding for four Ph.D. students working on novel research topics that fit in with CRNCH’s mission of post-Moore software and hardware designs. Fellows are required to create a poster and paper during their term, and also present at the annual CRNCH Summit in January of each year. This gives them the opportunity prepare for publication and the job market.

CRNCH is a research center that focuses on exploring new computing paradigms after the end of Moore’s law, sometimes called the post-Moore’s era. The center partners with academics and industry to explore full-stack solutions on everything from quantum computing to approximate computation.

Applications for the spring CRNCH fellowship are due Dec. 18, 2020.

Meet the fellows:

Dingtian Zhang
School: School of Interactive Computing
Advisor: Professor Gregory Abowd

Why did you apply for the CRNCH fellowship? 
I am developing computational materials that can weave into the fabric of everyday objects. My work falls under the categories of analog computing, computing based on novel device physics and materials, and optical computing, which is in line with the interest of CRNCH.

What project will you be working on during the fellowship?
We are particularly interested in developing large-scaled sensing systems that can perform light-based sensing on the surfaces of everyday objects to detect implicit and explicit human activities. Such systems need to be self-sustained and easy to maintain, cost effective to scale, conformal to everyday objects, and protective of user privacy. Conventional vision systems based on cameras struggle to keep up with the ubiquitous deployment on these dimensions.

We are developing computational photodetectors that not only sense, but also process the signal in the analog domain to extract mid-level vision features, reducing the inherent complexity and latency from digital signal acquisition and computing. This does not only make the system low-power and scalable, but also prevents capturing unwanted information from images. We adopt emerging organic semiconductor (OSC) devices in fabricating computational photodetectors with lightweight, thin, flexible, and conformal form factors. Computational photodetectors will enable a wide range of large-scale applications such as smart environment, health monitoring, asset tracking, and activity recognition.

Muliang Zhu
School: School of Electrical and Computer Engineering
Advisor: Professor Ali Adibi

Why did you apply for the CRNCH fellowship?
Being part of a larger community that focuses on new frontiers of computing technology is a great benefit for all people like me in the optical computing area. Because of this, I applied for the fellowship to bring the concept of computing using ultracompact photonic devices to CRNCH.

What project will you be working on during the fellowship?
I will be working on nanostructure optical parametric oscillators (OPOs) for nonlinear control of light at the subwavelength scale, aiming at using photonics for neural-network-type computing. The main part of the project I am currently focusing on is the development of nonlinear meta-structure that can provide the optical nonlinearity that is needed for the development of any brain-inspired computing.

Chunxing Yin
School: School of Computational Science and Engineering
Advisor: Professor Rich Vuduc

Why did you apply for the CRNCH fellowship?
My research focuses on neural networks compression using tensorization, which offers a systematic way to trade-off storage, execution time, and accuracy with respect to the capabilities of a given hardware platform. My advisor and I believe that this work fits well within CRNCH and would benefit from feedback from the CRNCH community, so we applied for this fellowship.

What project will you be working on during the fellowship?
We propose to evaluate to what extent convolutional layers and embedding layers of recommender systems can be trained in a reduced form using the techniques of low-rank tensor train decomposition.

Recent studies have shown an alarming growth in the environmental burden from AI, for example the number of parameters in state-of-the-art language models increased to over 175 billion for OpenAI’s GPT-3. To significantly reduce the environmental footprint of AI, we need order-of-magnitude reduction in the infrastructure demand while maintaining or even outperforming state-of-the-art model accuracy. We are exploring a new algorithmic approach, tensor train decomposition, to cope with the large memory requirement of DNNs. The core idea is to replace large weight tensors with a sequence of small tensor decompositions that trades of memory storage with computation. Initially, we will study the compressed networks in the context of heterogeneous CPU-GPU architectures. But we believe that our results will help guide engineering co-design of future hardware-software systems for neural networks.