the image of me

Vahideh Akhlaghi

PhD Candidate
Computer Science and Engineering
UC San Diego

About Me

I am currently a PhD candidate in the department of Computer Science and Engineering, at UC San Diego. I am working in Microelectronic Embedded System Laboratory (MESL), led by Prof. Rajesh Gupta.
I received my Master and Bachelor Degree in Computer Engineering from University of Tehran.

Resume

You can find my resume here.

Work Experience

Research Assistant, UCSD, 2014-2020.

Teaching Assistant, UCSD, Winter 2020.

Research Intern, Microsoft Research, Spring 2019.

Research Intern, Microsoft Research, Summer 2018.

Research

Model-hardware Co-optimization for Deep Learning Accelerators

Platform-aware model-hardware co-approximation to improve DNNs execution costs

Model compression to improve distributed training of Convolutional Neural Networks (CNN)

Improving computation complexity of deep Convolutional Neural Networks (CNN) for improved training and inference cost

Design Automation for Approximate Computing

Machine Learning-based optimization framework for energy-error trade-offs in combining approximate methods in approximate computing

Error-bounded function approximation with probabilistic data structure

In-memory Computing

In-memory implementation of tree-based learning algorithms using resistive memories

Publications

H. Omidvar, V. Akhlaghi, H. Su, M. Franceschetti and R. Gupta, “Associative Convolutional Layers”, submitted to International Conference on Artificial Intelligence and Statistics (AISTATS), 2020. PDF

J. H. Lin, A. Lotfi, V. Akhlaghi, Z. Tu, and R. Gupta, “Accelerating Local Binary Pattern Networks with Software-Programmable FPGAs”, IEEE Design, Automation and Test in Europe Conference (DATE), 2019. PDF

V. Akhlaghi*, A. Yazdanbakhsh*, K. Samadi, R. K. Gupta, H. Esmaeilzadeh, “SnaPEA: Predictive Early Activation for Reducing Computation in Deep Convolutional Neural Networks”, International Symposium on Computer Architecture (ISCA), 2018 (*equal contributions). PDF

V. Akhlaghi, S. Gao, R. K. Gupta, “LEMAX: Learning-based Energy Consumption Minimization in Approximate Computing with Quality Guarantee”, ACM/IEEE Design Automation Conference (DAC), 2018. PDF

X. Jiao, V. Akhlaghi, Y. Jiang, R. K. Gupta, “Energy-Efficient Neural Networks using Approximate Computation Reuse”, IEEE Design, Automation, and Test in Europe (DATE), 2018.

V. Akhlaghi, A. Rahimi, R. K. Gupta, “Resistive Bloom Filters: From Approximate Membership to Approximate Computing with Bounded Errors”, IEEE Design, Automation and Test in Europe (DATE), 2016.

J. Koh, B. Balaji, V. Akhlaghi, Y. Agarwal, R. K. Gupta, “Quiver: Using Control Perturbations to Increase the Observability of Sensor Data in Smart Buildings. CoRR abs/1601.07260 (2016)

V. Akhlaghi, M. Kamal, A. Afzali-kusha, M. Pedram, “An Efficient Network on-Chip Architecture Based on Isolating Local and non-Local Communications”, Elsevier Journal of Computers and Electrical Engineering, 2015.

V. Akhlaghi, M. Kamal, A. Afzali-kusha, M. Pedram, “An Efficient Network on-Chip Architecture Based on Isolating Local and non-Local Communications”, IEEE Design Automation and Test in Europe (DATE ‘13), 2013.

Poster Presentations

V. Akhlaghi, M. Imani, T. S. Rosing, R. K. Gupta. Acceleration Architecture for Decision Tree Classification Algorithm. ACM/IEEE Design Automation Conference (DAC’17). Work in Progress.

V. Akhlaghi, A. Rahimi, R. K. Gupta, “Resistive Bloom Filters: From Approximate Membership to Approximate Computing with Bounded Errors”, Research Expo at UCSD, 2017.

V. Akhlaghi, H. Sohofi, Z. Navabi, “Migrating from RTL to ESL via SystemC Training”, IEEE/ACM Design Automation and Test in Europe (DATE ‘10), Germany, 2010, University Booth.

Contact Info

Email: vakhlaghi at eng dot ucsd dot edu
Office: 2150 EBU3b
Dept. of Computer Science and Engineering
9450 Gilman Drive, Mail Code 0404
La Jolla, CA 92093-0404