In-Memory Computing

projects funded by NSF and SRC

Non-Volatile In-Memory Processing Unit: Memory, In-Memory Logic and Deep Neural Network

The objective of this project is to explore leveraging emerging nanoscale spin-orbit torque magnetic random access memory (SOT-MRAM) to develop a non-volatile in-memory processing unit that could simultaneously work as non-volatile memory and a co-processor for next-generation energy efficient and high performance computing system. Such energy efficient in-memory computing system integrates logic and memory units by exploring innovations from emerging spintronic device technology to non-Von Neumann architecture, which is targeting to tackle power wall and memory wall bottlenecks in traditional computing system. It will be crucial for industry and academia to identify next-generation energy efficient and high performance computing platform design. The project also has education and outreach components including new curriculum in post-CMOS devices and circuits for undergraduate/graduate students, engineering outreach to diverse population and other underrepresented groups at the University of central Florida. The project will also directly involve minority and female graduate/ undergraduate students.

The proposed research requires synergistic exploration spanning from device technology to architecture innovation. Specifically, it consists of three research thrusts: (i) exploring novel SOT-MRAM memory array that could implement in-memory logic (AND/OR/XOR) without add-on logic circuits; (ii) investigating non-volatile in-memory processing unit (MPU) architecture that could simultaneously work as nonvolatile memory and co-processor to pre-process raw data within memory to accelerate data/computing intensive applications without sacrificing memory capacity; (iii) exploring MPU to implement in-memory convolution to greatly reduce data communication and accelerate state-of-the-art deep learning convolutional neural networks.

AlignMEM: Fast and Efficient DNA Sequence Alignment in Non-Volatile Magnetic RAM

The state-of-the-art DNA sequencing technologies could generate Terabytes of DNA sequence data in a single run, and their throughput is expected to increase 3-5 times each year in the coming years. In order to apply these big DNA-data into follow-up complex disease diagnostics/prognostics, such as cancer risk assessment, tailor patient treatment, and prenatal testing, they must be first aligned to a 3.2-billion-length human reference genome. However, the existing software tools for this purpose may need hours or days to align such large amount of DNA sequence data even with very powerful computing systems of today due to the ‘memory wall’ challenge in state-of-the-art computing architecture that describes the speed mismatch between memory units and computing units. To this end this, project leverages innovations from non-volatile nano-magnet based Magnetic Random Access Memory (MRAM) technology and in-memory computing architecture. If successful, it can achieve up to two orders magnitude higher computing performance, speed and energy efficiency for next-generation DNA sequence analysis system, which enables large-scale fast genomic data analytics to support research on various disease studies and biomedical applications. This project will develop new undergraduate/graduate level course modules on in-memory computing architecture and bioinformatics. 

This project will follow two main research tracks. The first one explores how to leverage the intrinsic non-volatile MRAM device property to efficiently develop ultra-parallel, reconfigurable in-memory logic required by DNA alignment computation and its big DNA-data Processing-in-Memory (PIM) accelerator architecture. The second research track will investigate how to develop fast DNA alignment-in-memory algorithm based on Burrows-Wheeler Transformation to match with the proposed MRAM based PIM platform and its large-scale genomic analysis application in disease phenotype prediction. Alignments generated will be used to estimate gene expression, and identify single nucleotide mutation events for patient samples, leading to molecular signatures for disease risk assessment.

Related Journal Publications

  1. [TCAS-II’21]  Jian Meng, Li Yang, Xiaochen Peng, Shimeng Yu, Deliang Fan and Jae-Sun Seo, “Structured Pruning of RRAM Crossbars for Efficient In-Memory Computing Acceleration of Deep Neural Networks,” IEEE Transactions on Circuits and Systems- II (TCAS-II) Vol. 68, No. 5, May 2021 [pdf]
  2. [TMAG’20] Shaahin Angizi, Zhezhi He, An Chen and Deliang Fan, “Hybrid Spin-CMOS Polymorphic Logic Gate with Application in In-Memory Computing,” IEEE Transactions on Magnetics (TMAG) , Volume: 56 , Issue: 2 , Feb. 2020, DOI: 10.1109/TMAG.2019.2955626 [pdf]
  3. [JETC’20] Zhezhi He, Li Yang, Shaahin Angizi, Adnan Siraj Rakin and Deliang Fan, “Sparse BD-Net: A Multiplication-Less DNN with Sparse Binarized Depth-wise Separable Convolution,” ACM Journal on Emerging Technologies in Computing Systems (JETC), January 2020 Article No.: 15 https://doi.org/10.1145/3369391 [pdf]
  4. [TCAD’19] Shaahin Angizi, Zhezhi He, Amro Awad and Deliang Fan, “MRIMA: An MRAM-based In-Memory Accelerator,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 27 March 2019, DOI: 10.1109/TCAD.2019.2907886  [pdf]
  5. [JETC’18] Farhana Parveen, Shaahin Angizi and Deliang Fan, “IMFlexCom: Energy Efficient In-memory Flexible Computing using Dual-mode SOT-MRAM,” ACM Journal on Emgerging Technologies in Computing Systems, Vol.14, no.3, Oct. 2018 [pdf]
  6. [TMSCS’18] Zhezhi He, Yang Zhang, Shaahin Angizi, Boqing Gong and Deliang Fan, “Exploring A SOT-MRAM based In-Memory Computing for Data Processing,” IEEE Transactions on Multi-Scale Computing Systems, 2018 [pdf]
  7. [TMAG’18] Farhana Parveen, Shaahin Angizi, Zhezhi He and Deliang Fan, “IMCS2: Novel Device-to-Architecture Co-design for Low Power In-memory Computing Platform using Coterminous Spin-Switch,” IEEE Transactions on Magnetics, vol. 54, no.7, July 2018 [pdf]
  8. [TCAD’17] S. Angizi, Z. He, N. Bagherzadeh and D. Fan, “Design and Evaluation of a Spintronic In-Memory Processing Platform for Non-Volatile Data Encryption,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol.37, no.9, Sept. 2018 [pdf]
  9. Shaahin Angizi, Deliang Fan, “Accelerating Bulk Bit-Wise X(N)OR Operation in Processing-in-DRAM Platform” arXiv:1904.05782April 2019

Related Conference Publications

  1. [DAC’21] Fan Zhang, Shaahin Angizi and Deliang Fan, “Max-PIM: Fast and Efficient Max/Min Searching in DRAM” In: 58th Design Automation Conference (DAC), San Francisco, CA, July 11-15, 2021 ( –Best Paper Nomination–)
  2. [DAC’21] Fan Zhang, Shaahin Angizi, Naima Ahmed Fahmi, Wei Zhang and Deliang Fan, “PIM-Quantifier: A Processing-in-Memory Platform for Genome Quantification”. In: 58th Design Automation Conference (DAC), San Francisco, CA, July 11-15, 2021
  3. [SOCC’20]  Li Yang, Zhezhi He, Shaahin Angizi and Deliang Fan, “Processing-In-Memory Accelerator for Dynamic Neural Network with Run-Time Tuning of Accuracy, Power and Latency,” 33rd IEEE International System-on-Chip Conference (SOCC), September 8-11, 2020 (invited)
  4. [GLSVLSI’20] Shaahin Angizi, Wei Zhang and Deliang Fan, “Exploring DNA Alignment-in-Memory Leveraging EmergingSOT-MRAM”, 30th edition of the ACM Great Lakes Symposium on VLSI (GLSVLSI), September 7-9, 2020 (invited) [pdf]
  5. [DAC’20] Shaahin Angizi, Naima Ahmed Fahmi, Wei Zhang and Deliang Fan, “PIM-Assembler: A Processing-in-Memory Platform for Genome Assembly” In: 57th Design Automation Conference (DAC), San Francisco, CA, July 19-23, 2020
  6. [DATE’20] Shaahin Angizi, Jiao Sun, Wei Zhang and Deliang Fan, “PIM-Aligner: A Processing-in-MRAM Platform for Biological Sequence Alignment,” Design, Automation and Test in Europe (DATE), 09-13 March 2020, ALPEXPO, Grenoble, France
  7. [ASPDAC’20] Li Yang, Shaahin Angizi, Deliang Fan, “A Flexible Processing-in-Memory Accelerator for Dynamic Channel-Adaptive Deep Neural Networks,” Asia and South Pacific Design Automation Conference (ASP-DAC), Jan. 13-16, 2020, Beijing, China 
  8. [ICCAD’19] Shaahin Angizi and Deliang Fan, “ReDRAM: A Reconfigurable Processing-in-DRAM Platform for Accelerating Bulk Bit-Wise Operations,” IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 4-7 November 2019, Westminster, CO
  9. [NANOARCH’19] Shaahin Angizi and Deliang Fan, “Deep Neural Network Acceleration in Non-Volatile Memory: A Digital Approach?,” IEEE/ACM International Symposium on Nanoscale Architectures, 17-19 July 2019, Qingdao, CHINA
  10. [ISVLSI’19] Shaahin Angizi, Zhezhi He, Dayane Reis, Xiaobo Sharon Hu, Wilman Tsai, Shy Jay Lin and Deliang Fan, “Accelerating Deep Neural Networks in Processing-in-Memory Platforms: Analog or Digital Approach?,” IEEE Computer Society Annual Symposium on VLSI, 15 – 17 July 2019, Miami, Florida, USA (invited)
  11. [Dagstuhl Report’19] Deliang Fan, “Cognitive Computing-in-Memory: Circuit to Algorithm,” Dagstuhl Seminar 19152, Emerging Hardware Techniques and EDA Methodologies for Neuromorphic Computing, Germany, 2019
  12. [GLSVLSI’19] Shaahin Angizi and Deliang Fan, “GraphiDe: A Graph Processing Accelerator leveraging In-DRAM-Computing,” ACM Great Lakes Symposium on VLSI(GLSVLSI), May 9-11, 2019, Washington, D.C. USA ( Best Paper Award[pdf]
  13. [DAC’19] Shaahin Angizi, Jiao Sun, Wei Zhang and Deliang Fan, “AlignS: A Processing-In-Memory Accelerator for DNA Short Read Alignment Leveraging SOT-MRAM,” Design Automation Conference (DAC), June 2-6, 2019, Las Vegas, NV, USA [pdf]
  14. [DAC’19] Zhezhi He, Jie Lin, Rickard Ewetz, Jiann-Shiun Yuan and Deliang Fan, “Noise Injection Adaption: End-to-End ReRAM Crossbar Non-ideal Effect Adaption for Neural Network Mapping,” Design Automation Conference (DAC), June 2-6, 2019, Las Vegas, NV, USA [pdf] [code in GitHub]
  15. [DATE’19] Shaahin Angizi, Jiao Sun, Wei Zhang and Deliang Fan, “GraphS: A Graph Processing Accelerator Leveraging SOT-MRAM,” Design, Automation and Test in Europe (DATE), March 25-29, 2019, Florence, Italy. [pdf]
  16. [ASPDAC’19] Shaahin Angizi, Zhezhi He and Deliang Fan, “ParaPIM: A Parallel Processing-in-Memory Accelerator for Binary-Weight Deep Neural Networks,” Asia and South Pacific Design Automation Conference (ASP-DAC), Jan. 21-24, 2019, Tokyo, Japan [pdf]
  17. [ICCD’18] Adnan Siraj Rakin, Shaahin Angizi, Zhezhi He and Deliang Fan, “PIM-TGAN: A Processing-in-Memory Accelerator for Ternary Generative Adversarial Networks,” IEEE International Conference on Computer Design (ICCD) , Oct. 7-10, 2018, Orlando, FL, USA [pdf]
  18. [ICCAD’18] Shaahin Angizi, Zhezhi He and Deliang Fan, “DIMA: A Depthwise CNN In-Memory Accelerator,” IEEE/ACM International Conference on Computer Aided Design, Nov. 5-8, 2018, San Diego, CA, USA [pdf]
  19. [ISVLSI’18] Zhezhi He, Shaahin Angizi, Adnan Siraj Rakin and Deliang Fan, “BD-NET: A Multiplication-less DNN with Binarized Depthwise Separable Convolution,” IEEE Computer Society Annual Symposium on VLSI, July 9-11, 2018, Hong Kong, CHINA [pdf] ( Best Paper Award)  
  20. [ISVLSI’18] Zhezhi He, Shaahin Angizi and Deliang Fan, “Accelerating Low Bit-Width Deep Convolution Neural Network in MRAM,” IEEE Computer Society Annual Symposium on VLSI, July 9-11, 2018, Hong Kong, CHINA (invited) [pdf]
  21. [DAC’18] Shaahin Angizi*, Zhezhi He*, Adnan Siraj Rakin and Deliang Fan, “CMP-PIM: An Energy-Efficient Comparator-based Processing-In-Memory Neural Network Accelerator,” IEEE/ACM Design Automation Conference (DAC), June 24-28, 2018, San Francisco, CA, USA (* The first two authors contributed equally) [pdf]
  22. [DAC’18] Shaahin Angizi, Zhezhi He and Deliang Fan, “PIMA-Logic: A Novel Processing-in-Memory Architecture for Highly Flexible and Energy-Efficient Logic Computation, ” IEEE/ACM Design Automation Conference (DAC), June 24-28, 2018, San Francisco, CA, USA [pdf]
  23. [ASPDAC’18] F. Parveen, Z. He, S. Angizi and D. Fan, “HieIM: Highly Flexible In-Memory Computing using STT MRAM,” Asia and South Pacific Design Automation Conference (ASP-DAC), Jan. 22-25, 2018, Jeju Island, Korea [pdf]
  24. [ASPDAC’18] S. Angizi, Z. He, F. Parveen and D. Fan, “IMCE: Energy-Efficient Bit-Wise In-Memory Convolution Engine for Deep Neural Network,” Asia and South Pacific Design Automation Conference (ASP-DAC), Jan. 22-25, 2018, Jeju Island, Korea [pdf]
  25. [ICCD’17] Z. He, S. Angizi and D. Fan, “Exploring STT-MRAM based In-Memory Computing Paradigm with Application of Image Edge Extraction,” IEEE International Conference on Computer Design (ICCD) , Nov. 5-8, 2017, Boston, MA [pdf]
  26. [ICCD’17] D. Fan and S. Angizi “Energy Efficient In-Memory Binary Deep Neural Network Accelerator with Dual-Mode SOT-MRAM,” IEEE International Conference on Computer Design (ICCD) , Nov. 5-8, 2017, Boston, MA [pdf]
  27. [NCAMA’17] S. Angizi and D. Fan , “IMC: Energy-Efficient In-Memory Convolver for Accelerating Binarized Deep Neural Network,” Neuromorphic Computing Symposium: Architectures, Models, and Applications , July 17-19, 2017, Knoxville, Tennessee [pdf]
  28. [ISLPED’17] F. Parveen, S. Angizi, Z. He and D. Fan , “Low Power In-Memory Computing based on Dual-Mode SOT-MRAM,” IEEE/ACM International Symposium on Low Power Electronics and Design, July 24-26, 2017, Taipei, Taiwan [pdf]
  29. [NANOARCH’17] Z. He, S. Angizi, F. Parveen and D. Fan , “High Performance and Energy-Efficient In-Memory Computing Architecture based on SOT-MRAM,” IEEE/ACM International Symposium on Nanoscale Architectures , July 25-26, 2017, Newport, USA [pdf]
  30. [ISVLSI’17] D. Fan, S. Angizi and Z. He, “In-Memory Computing with Spintronic Devices,” IEEE Computer Society Annual Symposium on VLSI (ISVLSI), July 3-5, 2017, Bochum, Germany (invited) [pdf]
  31. [ISVLSI’17]S. Angizi, Z. He, F. Parveen and D. Fan, “RIMPA: A New Reconfigurable Dual-Mode In-Memory Processing Architecture with Spin Hall Effect-Driven Domain Wall Motion Device,” IEEE Computer Society Annual Symposium on VLSI (ISVLSI), July 3-5, 2017, Bochum, Germany [pdf]
  32. [ISVLSI’17]F. Parveen, Z. He, S. Angizi and D. Fan, “Hybrid Polymorphic Logic Gate with 5-Terminal Magnetic Domain Wall Motion Device,” IEEE Computer Society Annual Symposium on VLSI (ISVLSI), July 3-5, 2017, Bochum, Germany [pdf] ( Best Paper Award)
  33. [MWSCAS’17]D. Fan, Z. He and S. Angizi, “Leveraging Spintronic Devices for Ultra-Low Power In-Memory Computing: Logic and Neural Network,” 60th IEEE International Midwest Symposium on Circuits and Systems, Aug. 6-9, 2017, Boston, MA, USA (invited) [pdf]
  34. [ISCAS’17]F. Parveen, S. Angizi, Z. He and D. Fan, “Hybrid Polymorphic Logic Gate Using 6 Terminal Magnetic Domain Wall Motion Device,” IEEE International Symposium on Circuits & Systems (ISCAS), Baltimore, MD, USA, May 28-31, 2017 [pdf]
  35. [GLSVLSI’17]Z. He, S. Angizi, F. Parveen, and D. Fan, “Leveraging Dual-Mode Magnetic Crossbar for Ultra-low Energy In-Memory Data Encryption”, 27th GLSVLSI, Banff, Alberta, Canada, May 10-12, 2017 [pdf]
  36. [GLSVLSI’17]S. Angizi, Z. He, and D. Fan, “Energy Efficient In-Memory Computing Platform Based on 4-Terminal Spin Hall Effect-Driven Domain Wall Motion Devices”, 27th GLSVLSI, Banff, Alberta, Canada, May 10-12, 2017 [pdf]
  37. [NANOARCH’16]D. Fan, “Low Power In-Memory Computing Platform with Four Terminal Magnetic Domain Wall Motion Devices”, IEEE/ ACM International Symposium on Nanoscale Architectures, , Beijing, China, July 18-20, 2016 [pdf]