The EPIC Lab’s research on AI/ML hardware acceleration explores radically new computing substrates—silicon photonics, optical processing‑in‑memory, and stochastic in‑memory computing—to overcome the energy, bandwidth, and scalability bottlenecks of electronic accelerators. This work spans the design of photonic tensor cores, coherent and non‑coherent photonic neural networks, optical processing‑in‑memory architectures, and mixed analog–stochastic in‑DRAM accelerators that target a wide range of models including Transformers, diffusion models, GANs, CNNs, RNNs, GNNs, and large language models. The lab develops cross‑layer solutions that integrate device‑level innovations (e.g., MZI‑based photonic circuits, PCM‑based photonic elements, polarization‑domain computing) with architecture‑level techniques such as neural architecture search, pruning, quantization, and secure optical computing. This research also advances sustainable AI by enabling ultra‑low‑energy stochastic photonic computing, in‑flash bitwise processing, and chiplet‑scale photonic interconnects. Collectively, these contributions establish a comprehensive foundation for next‑generation AI accelerators that deliver orders‑of‑magnitude improvements in speed and efficiency while remaining scalable, robust, and well‑suited for the computational demands of modern machine learning.
Selected Publications
S. Afifi, O. Alo, I. Thakkar, S. Pasricha, “Sustainable Transformer Neural Network Acceleration with Stochastic Photonic Computing”, IEEE Summer Topicals Meeting Series (SUM), Tulum, Mexico, Jul 2026.
T. Suresh, S. Afifi, S. Pasricha, “Accelerating Diffusion Models for Generative AI Applications with Silicon Photonics”, IEEE/ACM Design, Automation and Test in Europe (DATE) Conference, Verona, Italy, Mar 2026.
B. T. Magar, S. Afifi, I. Thakkar, S. Pasricha, “In-DRAM Stochastic Computing for Energy-Efficient Acceleration of Transformer Neural Networks”, Energy Consequences of Information Workshop, Santa Fe, NM, Feb 2026.
S. Afifi, O. Alo, I. Thakkar, S. Pasricha, “Energy-Efficient Acceleration of Transformers with Stochastic Photonic Computing”, Energy Consequences of Information Workshop, Santa Fe, NM, Feb 2026.
T. Suresh, S. Afifi, S. Pasricha, “DiffLight: Generative Diffusion Model Acceleration with Silicon Photonics”, IEEE Design & Test, 2026.
T. Suresh, S. Afifi, S. Pasricha, “Sustainable Acceleration of Generative AI Neural Network Models with Silicon Photonics”, IEEE ICCD, Dallas, TX, Nov 2025.
H. Ur-Rahman, T. Suresh, S. Pasricha, B. Ray, “TCFlash: In-Flash Bulk Bitwise Processing via Dynamic Sensing and TLC Encoding in 3D NAND”, IEEE ICCD, Dallas, TX, Nov 2025.
S. Afifi, O. Alo, I. Thakkar, S. Pasricha, “A Light-Speed Large Language Model Accelerator with Optical Stochastic Computing”, ACM Great Lakes Symposium on VLSI (GLSVLSI), New Orleans, LA, 2025.
A. Shafiee, F. Sunny, S. Pasricha, M. Nikdast, “LuxNAS: A Coherent Photonic Neural Network Powered by Neural Architecture Search”, CLEO Symposium, May 2025.
T. Suresh, S. Afifi, S. Pasricha, “PhotoGAN: Generative Adversarial Neural Network Acceleration with Silicon Photonics” IEEE International Symposium on Quality Electronic Design (ISQED), 2025.
A. Shafiee, L. Chen, S. Pasricha, J. Yao, M. Nikdast, “Enabling Scalable Photonic Tensor Cores with Polarization-Domain Photonic Computing”, OFC Symposium, Apr 2025.
S. Afifi, I. Thakkar, S. Pasricha, “SafeLight: Enhancing Security in Optical Convolutional Neural Network Accelerators”, IEEE/ACM Design, Automation and Test in Europe (DATE) Conference, Mar 2025.
S. Afifi, O. Alo, I. Thakkar, S. Pasricha, “ASTRA: A Stochastic Transformer Neural Network Accelerator with Silicon Photonics”, ACM Transactions on Embedded Computing Systems (TECS), 2025.
A. Shafiee, Z. Ghanaatianjobzari, B. Charbonnier, C. A. Ríos Ocampo, S. Pasricha, and M. Nikdast, “PCM-based silicon photonic neural networks under fabrication nonuniformity”, SPIE Photonics West, San Francisco, CA, Jan 2025.
S. Afifi, I. Thakkar, S. Pasricha, “STAR: A Mixed Analog Stochastic In-DRAM Convolutional Neural Network Accelerator”, IEEE Design & Test, 2024.
S. Afifi, I. Thakkar, S. Pasricha, “ARTEMIS: A Mixed Analog-Stochastic In-DRAM Accelerator for Transformer Neural Networks”, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 2024.
S. Afifi, S. Pasricha, M. Nikdast, “Shedding Light on LLMs: Harnessing Photonic Neural Networks for Accelerating LLMs”, IEEE ICCAD, Nov 2024.
F. Sunny, A. Shaifee, A. Balasubramaniam, M. Nikdast, S. Pasricha, “OPIMA: Optical Processing-In-Memory for Convolutional Neural Network Acceleration”, IEEE/ACM CODES+ISSS (ESWEEK), Oct 2024.
F. Sunny, E. Taheri, M. Nikdast, S. Pasricha, “Silicon Photonic Network-on-Interposer Design for Energy Efficient Convolutional Neural Network Acceleration on 2.5D Chiplet Platforms”, IEEE/ACM DATE, Mar 2024.
S. Afifi, F. Sunny, M. Nikdast, S. Pasricha, “Accelerating Neural Networks for Large Language Models and Graph Processing with Silicon Photonics”, IEEE/ACM DATE, Mar 2024.
F. Sunny, A. Shaifee, A. Balasubramaniam, M. Nikdast, S. Pasricha, “OPIMA: Optical Processing-In-Memory for Convolutional Neural Network Acceleration”, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 2024.
A. Shafiee, S. Banerjee, K. Chakrabarty, S. Pasricha, M. Nikdast, “Analysis of Optical Loss and Crosstalk Noise in MZI-based Coherent Photonic Neural Networks”, IEEE/OPTICA Journal of Lightwave Technology (JLT), 2024.
S. Afifi, F. Sunny, A. Shafiee, M. Nikdast, S. Pasricha, “GHOST: A Graph Neural Network Accelerator using Silicon Photonics”, ACM Transactions on Embedded Computing Systems (TECS), 2023.
A. Shafiee, S. Banerjee, B. Charbonnier, S. Pasricha, and M. Nikdast, “Compact and Low-Loss PCM-based Silicon Photonic MZIs for Photonic Neural Networks,” IEEE Photonics Conference (IPC), Orlando, FL, Nov 2023.
S. Afifi, F. Sunny, A. Shafiee, M. Nikdast, S. Pasricha, “GHOST: A Graph Neural Network Accelerator using Silicon Photonics”, IEEE/ACM CASES (ESWEEK), Oct 2023.
S. Afifi, F. Sunny, M. Nikdast, S. Pasricha, “TRON: Transformer Neural Network Acceleration with Non-Coherent Silicon Photonics”, ACM GLSVLSI, 2023.
F. Sunny, M. Nikdast, S. Pasricha, “Cross-Layer Design for AI Acceleration with Non-Coherent Optical Computing”, ACM GLSVLSI, 2023.
C. Ogbogu, M. Abernot, C. Delacour, A. Todri-Sanial, S. Pasricha, P. P. Pande, “Energy-Efficient Machine Learning Acceleration: From Technologies to Circuits and Systems”, Proc. IEEE ISLPED, Vienna, Austria, Aug 2023.
S. Banerjee, K. Chakrabarty, S. Pasricha, M. Nikdast, “Pruning Coherent Integrated Photonic Neural Networks”, IEEE Journal of Selected Topics in Quantum Electronics, (JSTQE), 2023.
F. Sunny, E. Taheri, M. Nikdast, S. Pasricha, “Machine Learning Accelerators in 2.5D Chiplet Platforms with Silicon Photonics”, IEEE/ACM DATE, 2023.
M. Nikdast, S. Pasricha, K. Chakrabarty, “Silicon Photonic Neural Network Accelerators: Opportunities and Challenges”, CLEO, 2023.
A. Shafiee, S. Pasricha, M. Nikdast, “ Silicon Photonics for Future Computing Systems”, Wiley On-Line Encyclopedia, May 2022.
F. Sunny, M. Nikdast and S. Pasricha, “RecLight: A Recurrent Neural Network Accelerator With Integrated Silicon Photonics”, IEEE Computer Society Annual Symposium on VLSI (ISVLSI), 2022.
S. Banerjee, M. Nikdast, S. Pasricha, K. Chakrabarty, “Pruning Coherent Integrated Photonic Neural Networks Using the Lottery Ticket Hypothesis”, IEEE Computer Society Annual Symposium on VLSI (ISVLSI), 2022.
A. Shafiee, S. Banerjee, K. Chakrabarty, S. Pasricha and M. Nikdast, “LoCI: An Analysis of the Impact of Optical Loss and Crosstalk Noise in Integrated Silicon-Photonic Neural Networks”, ACM GLSVLSI, 2022.
F. Sunny, M. Nikdast and S. Pasricha, “A Silicon Photonic Accelerator for Convolutional Neural Networks with Heterogeneous Quantization”, ACM GLSVLSI, 2022.
S. Banerjee, M. Nikdast, S. Pasricha, K. Chakrabarty, “CHAMP: Coherent Hardware-Aware Magnitude Pruning of Integrated Photonic Neural Networks”, IEEE OFC, 2022.
F. Sunny, M. Nikdast, and S. Pasricha, “SONIC: A Sparse Neural Network Inference Accelerator with Silicon Photonics for Energy-Efficient Deep Learning”, IEEE/ACM Asia & South Pacific Design Automation Conference (ASPDAC), Jan 2022.
F. Sunny, A. Mirza, M. Nikdast, S. Pasricha, “ROBIN: A Robust Optical Binary Neural Network Accelerator”, IEEE/ACM CASES (ESWEEK), 2021.
F. Sunny, A. Mirza, M. Nikdast, S. Pasricha, “ROBIN: A Robust Optical Binary Neural Network Accelerator”, ACM Transactions on Embedded Computing Systems (TECS), Volume 20, Issue 5s, Oct 2021.
F. Sunny, E. Taheri, M. Nikdast, S. Pasricha, “A Survey on Silicon Photonics for Deep Learning”, ACM Journal on Emerging Technologies in Computing Systems (JETC), Vol. 17, Iss. 4, Oct 2021.
D. Dang, S. V. R. Chittamuru, S. Pasricha, R. Mahapatra, D. Sahoo, “BPLight-CNN: A Photonics-based Backpropagation Accelerator for Deep Learning”, ACM Journal on Emerging Technologies in Computing Systems (JETC), Vol. 17, Iss. 4, Oct 2021.
F. Sunny, A. Mirza, M. Nikdast, S. Pasricha, “CrossLight: A Cross-Layer Optimized Silicon Photonic Neural Network Accelerator”, IEEE/ACM Design Automation Conference (DAC), 2021