I am an incoming PhD student at the University of Washington in Seattle, Department of Electrical and Computer Engineering.

I graduated from the University of Southern California, Viterbi School of Engineering. I received my B.S in Electrical and Computer Engineering and my M.S. in Electrical Engineering in 2022.

My current/past research interests include:

• Optical/Photonic Computing
• Quantum Computing
• CMOS Neuromorphic Circuits
• Spiking Neural Networks
• Machine Learning Acceleration with Emerging Technologies
• Software/Hardware Co-design

My personal interests include reading sci-fi/fan-fi/detective novels, playing Hearthstone, cooking dark cuisines.

The complete CV can be found here: CV.pdf

Publications

• Parity-time symmetric optical neural networks
  • Haoqin Deng, Mercedeh Khajavikhan
  • Optica, Oct., 2021.
  • PDF, SLIDES
• AccQOC: Accelerating Quantum Optimal Control Based Pulse Generation
  • Jinglei Cheng, Haoqin Deng, Xuehai Qian
  • International Symposium on Computer Architecture (ISCA), May, 2020.
  • PDF, SLIDES

Researches:

• Optical computing
  Research Assistant; Supervised by Prof. Mercedeh Khajavikhan; Jun 2021 – present
  • Researched various architectures of Optical Neural Network(ONN), Quantum Optical Neural Network(QONN), Continuous/Discrete Variable Quantum Computation
  • Developed PT-ONN architecture with cascading PT-symmetric couplers, based on modulations of gain/loss contrasts; implemented simulations of on-chip training of PT-ONN using finite difference method with Python; achieved 68% training accuracy on MNIST dataset; simulated directional couplers in COMSOL; verified system transfer matrix of PT couplers with Mathematica
  • Reproduced on-chip training of MZI-based ONN with Python; achieved 71% on-chip training accuracy on MNIST dataset
  • Investigated the detrimental effect of V-shaped, intensity-dependent optical nonlinearity on VGG network architecture
• Spiking Neural Networks: Long Short-Term Memory (LSTM)
  Research Assistant, supervised by Prof. Peter Bereel; Aug 2021 - present
  • Developed custom Spiking LSTM that allows pipelined inferencing
  • Implemented custom Analog LSTM and Spiking LSTM module with PyTorch;
  • Implemented RNN-to-SNN conversion through weight transfer and spike-timing-dependent plasticity (STDP) training; achieved 85%, 96% testing accuracy on IMDB, temporal-MNIST datasets
• CMOS neuromorpic circuits
  Research Assistant; Supervised by Prof. Alice Parker; Jun 2021 – August 2021
  • Developed VLSI circuits of excitatory/inhibitory synapse, Axon Hillock, STDP-dopamine-noise synapse, dendritic spiking, edge detector, voltage adder, using Cadence; designed transistors to operate at subthreshold regime; obtained ideal biological waveforms for each circuit component
  • Designed a multi-layer neural network with edge-detection, STDP-synapse, and dopamine-modulation layers; used PyTorch to simulate the network topology, spike propagation, and synaptic weight update, with hardware-realistic parameters; achieved unity accuracy on self-designed four-class, nine-pixel dataset
  • Report
    
• Quantum computing
  Research Assistant; Supervised by Prof. Xuehai Qian; May 2019 – Sept 2020
  • Developed Accelerating Quantum Optimal Control (accQOC), a comprehensive compilation methodology that accelerates pulse generation by 9x:
    • partitioned the DAG of quantum circuit into sub-components under size constraints, using Qiskit
    • concatenated QOC pulses of each components using dynamic programming
    • balance partitionioned the MST of computing nodes for efficient parallel computation, using METIS
  • Worked on the optimization of VQE(Variational Quantum Eigen-solver) algorithm:
    • worked on circuit-level implementation of VQE with pyQpanda
    • optimized VQE measurement overhead through joint measurement of commuting Hamiltonians; projecting linearly-independent basis terms onto qubit computational basis using stabilizer formalism
    • worked on pulse-level optimization of VQE circuits using Qiskit open-pulse and QOC
• In-memory computing with InP memristor
  Research Assistant; Supervised by Prof. Rehan Kapadia; Jun 2020 – Jan 2021
  • Researched various architectures of mapping ANN/SNN onto hardware using semiconductor devices that mimic synapses, neurons
  • Simulated a temporal-encoded convolutional Spiking Neural Network(SNN) and a rate-encoded SNN with PyTorch, incorporating InP-synapse STDP parameters; achieved 89 & 91% training accuracies respectively, on MNIST dataset
  • Collaborated on designing Arduino/breadboard implementation of neural networks, utilizing a crossbar array of InP memristors
  • Grew InP using the Low-Temperature Templated Liquid-Phase(LT-TLP) technique

Internships

• Summer Internship, Shanghai AIKE Measurement Co. (July 2018 – August 2018)
  • Learned to use Labview to sample data and control hardware
  • Used Labview to automate a printer to print labels on a streamline
  • Assisted in designing the structure of a dispenser

Activities

• Mobile App: Go Eat
  • Developed a cross-platform app that recommends restaurants according to users’ preferences
  • Developed KNN and genetic models to recommend restaurants based on users’ and restaurants’ features
  • Implemented data fetching and storage using Firebase
• Desktop Game: Mind Palace
  • Developed a game that trains players to master ”Mind Palace” mnemonic technique
  • Designed game stages and implemented them with SDL library
  • Extracted key information from sentences with Google Cloud API
• Neural Signal Sampling
  • Collaborated on designing a neural-signal sampling device
  • Realized data transfer between FPGA and PC using Opal-Kelly’s API; implemented SPI protocol to transfer data between FPGAs; visualized data on PC end with matplotlib library

Class Projects

• Reinforcement Learning in Games
  • Developed customized gym-compatible, overhead-shooting game environment with pygame
  • Trained AI agents to achieve close to full score with Deep Q learning (DQN), written with PyTorch
  • Currently extending the current framework to allow multi-player collaboration
• CMOS VLSI
  • Implemented a 32 bit MAC unit with Brent-kung Adder and Array Multiplier;
  • Created schematics and drew layouts using Cadence
• Parallel Programming
  • Developed grayscale-to-color image conversion using CNN
  • Converted python model into C++ model using keras2cpp library
  • Parallelized convolution-layer computation using OpenMP and Pthread library
• Video Game Programming
  • Implemented 2D classical Arcade games such as Zelda, Super Mario, PAC-MAN
  • Implemented 3D games such as Mario Cars, FPS, Parkor game
  • Programmed game engines using C++ and SDL libraries
• FPGA Arcade Game “SPLATOON”
  • Designed a 2D-board Splatoon game that runs on a Xilinx FPGA Spartan 6, implemented with Verilog
  • Integrated joystick module for user control and VGA module for image display
• Web Game “Mission Universe”
  • Created a web-based, multiplayer jet-fighting game, using HTML, JavaScript, Java
  • Utilized Phaser API to construct game elements and WebSocket to transmit data in multiplayer mode
• Home Light System IoT Project
  • Developed a remote light control system running on Raspberry Pi
  • Fetched data from light sensor and transmit it through OpenMote
  • Utilized MQTT library to transmit data and control illumination
• Code Compiler
  • Implemented a compiler that parses a realistic program into tree structure, with Bison and C++
  • Generated assembly language and computed values of variables and memories
• Accelerometer
  • Developed an accelerometer running on Arduino
  • Integrated a rotary encoder to set threshold and a buzzer to present speed, controlled by interrupt

Skills:

Programing/Software:

• Python
• C/C++
• Java
• PyTorch
• Qiskit
• pyqpanda
• Verilog
• Cadence

Related Course

• EE514: Quantum Error Correction
• EE301: Linear Systems
• EE370: Electromagnetism
• EE354: Introduction to Digital Circuits
• EE348: Electronic Circuits
• EE451: Parallel and Distributed Computation
• EE457: Computer System Organization
• EE477: MOS VLSI Circuit Design
• EE459: Embedded System Design Laboratory
• EE520: Introduction to Quantum Computing
• EE582: Neuromorphic Computing
• EE540: Quantum Electronics
• CSCI527: Applied Machine Learning in Games
• CSCI201: Pinciple of Software Engineering
• CSCI270: Introduction to Algorithms and Theory of Computing
• CSCI104: Objected Oriented Programming
• CSCI353: Internetworking
• Math407: Propability Theory
• Math445: Mathematics of Physics and Engineering
• Phys438: Introduction to Quantum Mechanics
• Phys304: Mechanics
• ITP445: Professional C++
• ITP380: Video Game Programming