Princeton Inst for Computational Science & Eng (PICSciE)

Organization profile

Organization profile

The Princeton Institute for Computational Science and Engineering (PICSciE) is an interdisciplinary institute designed to bring together faculty and researchers from diverse backgrounds leveraging their broad expertise to address new and relevant computational problems and thereby contribute to the body of scientific knowledge.

Fingerprint Dive into the research topics where Princeton Inst for Computational Science & Eng (PICSciE) is active. These topic labels come from the works of this organization's members. Together they form a unique fingerprint.

collisions Physics & Astronomy
quarks Physics & Astronomy
leptons Physics & Astronomy
luminosity Physics & Astronomy
decay Physics & Astronomy
protons Physics & Astronomy
bosons Physics & Astronomy
cross sections Physics & Astronomy

Network Recent external collaboration on country level. Dive into details by clicking on the dots.

Research Output 1976 2020

PZnet: Efficient 3D ConvNet Inference on Manycore CPUs

Popovych, S., Buniatyan, D., Zlateski, A., Li, K. & Seung, H. S., Jan 1 2020, Advances in Computer Vision - Proceedings of the 2019 Computer Vision Conference CVC. Kapoor, S. & Arai, K. (eds.). Springer Verlag, p. 369-383 15 p. (Advances in Intelligent Systems and Computing; vol. 943).

Princeton University

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Program processors
Convolution
Magnetic resonance imaging
Image analysis
Microscopic examination

Weakly Supervised Deep Metric Learning for Template Matching

Buniatyan, D., Popovych, S., Ih, D., Macrina, T., Zung, J. & Seung, H. S., Jan 1 2020, Advances in Computer Vision - Proceedings of the 2019 Computer Vision Conference CVC. Kapoor, S. & Arai, K. (eds.). Springer Verlag, p. 39-58 20 p. (Advances in Intelligent Systems and Computing; vol. 943).

Princeton University

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Template matching
Supervised learning
Bandpass filters
Learning algorithms
Computer vision

A 64-Tile 2.4-Mb In-Memory-Computing CNN Accelerator Employing Charge-Domain Compute

Valavi, H., Ramadge, P. J., Nestler, E. & Verma, N., Jun 1 2019, In : IEEE Journal of Solid-State Circuits. 54, 6, p. 1789-1799 11 p., 8660469.

Princeton University

Research output: Contribution to journalArticle

Tile
Neurons
Particle accelerators
Convolution
Data storage equipment