Hadi Esmaeilzadeh

Assistant Professor

Georgia Institute of Technology
College of Computing
School of Computer Science

hadi [AT] cc [DOT] gatech [DOT] edu

266 Ferst Drive, KACB 2336
Atlanta, GA 30332-0765

Research Group: Alternative Computing Technologies (ACT) Laboratory

Curriculum Vitae Research Statement Teaching Statement

I joined the Georgia Tech's School of Computer Science as the inaugural holder of the Allchin Family early career professor in 2013. I founded the Alternative Computing Technologies (ACT) Lab to develop new technologies and cross-stack solutions for building the next generation computing systems.

I received my PhD in Computer Science and Engineering from University of Washington where I received the 2013 William Chan Memorial Dissertation Award.

My work has been recognized by four Communications of the ACM (CACM) Research Highlights, four IEEE Micro Top Picks, one honorable mention in IEEE Micro Top Picks, one nomination for CACM Research Highlights, and a Distinguished Paper award in HPCA 2016.

I have received the Air Force Young Investigator Award (2017), Google Research Faculty Award (2016 and 2014), Microsoft Research Award (2016), Qualcomm Research Award (2016), and Lockheed Inspirational Young Faculty Award (2016).

My students have received the Microsoft Research Fellowship (2016), Qualcomm Innovation Fellowship (2014), and the Georgia Tech President's Undergraduate Research Award (2015, 2016). My work on dark silicon has been profiled in New York Times.

I am eagerly looking for students that ambitiously want to make a difference! Please read my research statement before contacting me.

Project PHI: System Design for Pervasive Hierarchal Intelligence

Currently, we are focusing on Project PHI (Pervasive Hierarchical Intelligence), a holistic effort to provide a comprehensive solution for making immersive machine intelligence a reality.  Our guiding principle is to retain as much generality and automation while delivering large performance and efficiency gains through specialization and acceleration for a wide range of learning and intelligence workloads. As the first milestone of Project PHI, we have developed Tabla, which is open source and available at http://act-lab.org/artifacts/tabla/. This cross-stack solution - spanning from programming language to the hardware - rethinks the hardware/software abstraction by delving into the theory of machine learning. It leverages the insight that many learning algorithms can be solved using stochastic gradient descent that minimizes an objective function. The solver is fixed while the objective function changes with the learning algorithm. Therefore, Tabla uses stochastic optimization as the abstraction between hardware and software. Consequently, programmers specify the learning algorithm by merely expressing the gradient of the objective function in our domain specific language. Tabla then automatically generates the synthesizable implementation of the accelerator for scale-out FPGA realization using a set of template designs. Real hardware measurements show orders of magnitude higher performance and power efficiency while the programmer only writes 60 lines of code. These encouraging results show that rethinking the hardware/software abstractions from an algorithmic perspective can open new dimensions in system design for Pervasive Hierarchical Intelligence.


PhD Students

MS Students

Undergraduate Students


Recent Publications:

For a full list, please check out my Google Scholar profile!


Guest Performer. Concert in Dashti. Bereket UT-Austin Middle Eastern Ensemble, Butler School of Music, The University of Texas at Austin, Bates Concert Hall, April 2010