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Real-world Processing-in-Memory Architectures

Tutorial Description

Processing-in-Memory (PIM) is a computing paradigm that aims at overcoming the data movement bottleneck (i.e., the waste of execution cycles and energy resulting from the back-and-forth data movement between memory units and compute units) by making memory compute-capable.

Explored over several decades since the 1960s, PIM systems are becoming a reality with the advent of the first commercial products and prototypes.

A number of startups (e.g., UPMEM, Neuroblade) are already commercializing real PIM hardware, each with its own design approach and target applications. Several major vendors (e.g., Samsung, SK Hynix, Alibaba) have presented real PIM chip prototypes in the last two years.

Most of these architectures have in common that they place compute units near the memory arrays. But, there is more to come: Academia and Industry are actively exploring other types of PIM by, e.g., exploiting the analog operation of DRAM, SRAM, flash memory and emerging non-volatile memories.

PIM can provide large improvements in both performance and energy consumption, thereby enabling a commercially viable way of dealing with huge amounts of data that is bottlenecking our computing systems. Yet, it is critical to examine and research adoption issues of PIM using especially learnings from real PIM systems that are available today.

This tutorial focuses on the latest advances in PIM technology. We will (1) provide an introduction to PIM and taxonomy of PIM systems, (2) give an overview and a rigorous analysis of existing real-world PIM hardware, (3) conduct hand-on labs using real PIM systems, and (4) shed light on how to enable the adoption of PIM in future computing systems.


YouTube livestream


Agenda (February 26, 2023)

  • 8:00am-8:40am, Prof. Onur Mutlu, “Memory-centric computing: Introduction to PIM as a paradigm to overcome the data movement bottleneck”.
    • PIM taxonomy: PNM (processing near memory) and PUM (processing using memory).
  • 8:40am-10:00am, Dr. Juan Gómez Luna, “Processing-Near-Memory: Real PNM Architectures Programming General-purpose PIM”.
    • PNM prototypes: Samsung HBM-PIM, SK Hynix AiM, Samsung AxDIMM.
    • UPMEM PIM: Architecture and Programming.
  • Coffee break (10:00am-10:20am)
  • 10:20am-11:00am, Dr. Dimin Niu, “A 3D Logic-to-DRAM Hybrid Bonding Process-Near-Memory Chip for Recommendation System”.
  • 11:00am-11:40pm, Dr. Christina Giannoula, “SparseP: Towards Efficient Sparse Matrix Vector Multiplication on Real Processing-In-Memory Architectures”.
  • 11:40pm-12:20pm, Dr. Juan Gómez Luna, “Processing-Using-Memory: Exploiting the Analog Operational Properties of Memory Components”.
  • Lunch break (12:20pm-1:20pm)
  • 1:20pm-2:00pm, Dr. Manuel Le Gallo, “Deep learning inference using computational phase-change memory”.
  • 2:00pm-2:40pm, Dr. Juan Gómez Luna, “Adoption issues: How to enable PIM?”
  • 2:40pm-3:20pm, Dr. Juan Gómez Luna, “Introduction/preparation for hands-on labs”.
  • Coffee break (3:20pm-3:40pm)
  • Hands-on Lab (3:40pm-5:40pm)
    • Programming and understanding a real processing-in-memory architecture.
Time Speaker Title Materials
8:00am-8:40am Prof. Onur Mutlu Memory-Centric Computing (PDF) (PPT)
8:40am-10:00am Dr. Juan Gómez Luna Processing-Near-Memory: Real PNM Architectures Programming General-purpose PIM (PDF) (PPT)
10:20am-11:00am Dr. Dimin Niu A 3D Logic-to-DRAM Hybrid Bonding Process-Near-Memory Chip for Recommendation System
11:00am-11:40am Dr. Christina Giannoula SparseP: Towards Efficient Sparse Matrix Vector Multiplication on Real Processing-In-Memory Architectures (PDF) (PPT)
1:30pm-2:10pm Dr. Juan Gómez Luna Processing-Using-Memory: Exploiting the Analog Operational Properties of Memory Components (PDF) (PPT)
2:10pm-2:50pm Dr. Manuel Le Gallo Deep Learning Inference Using Computational Phase-Change Memory
2:50pm-3:30pm Dr. Juan Gómez Luna PIM Adoption Issues: How to Enable PIM Adoption? (PDF) (PPT)
3:40pm-5:40pm Dr. Juan Gómez Luna Hands-on Lab: Programming and Understanding a Real Processing-in-Memory Architecture (Handout)

Learning Materials

  • Gómez-Luna, J., and Mutlu, O., Data-Centric Architectures: Fundamentally Improving Performance and Energy (227-0085-37L), ETH Zürich, Fall 2022.
  • Mutlu, O., Ghose, S., Gómez-Luna, J., and Ausavarungnirun, R. A Modern Primer on Processing in Memory. In Emerging Computing: From Devices to Systems, 2023.
  • Gómez-Luna, J., El Hajj, I., Fernandez, I., Giannoula, C., Oliveira, G. F., and Mutlu, O. (2022). Benchmarking a New Paradigm: Experimental Analysis and Characterization of a Real Processing-in-Memory System. IEEE Access, 2022.
  • Giannoula, C., Fernandez, I., Gómez-Luna, J., Koziris, N., Goumas, G., and Mutlu, O. SparseP: Towards Efficient Sparse Matrix Vector Multiplication on Real Processing-In-Memory Architectures. SIGMETRICS 2022.
  • Olgun, A., Gómez-Luna, J., Kanellopoulos, K., Salami, B., Hassan, H., Ergin, O., and Mutlu, O. PiDRAM: A Holistic End-to-end FPGA-based Framework for Processing-in-DRAM. ACM TACO, 2022.

More Learning Materials

start.txt · Last modified: 2023/08/16 14:18 by ewent

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