Enhancing FPGAs with Magnetic Tunnel Junction-Based Block RAMs

Author:

Tatsumura Kosuke1ORCID,Yazdanshenas Sadegh2,Betz Vaughn2

Affiliation:

1. Toshiba Corporation, Kawasaki, Japan

2. University of Toronto, Toronto, Ontario, Canada

Abstract

While plentiful on-chip memory is necessary for many designs to fully utilize an FPGA’s computational capacity, SRAM scaling is becoming more difficult because of increasing device variation. An alternative is to build FPGA block RAM (BRAM) from magnetic tunnel junctions (MTJ), as this emerging embedded memory has a small cell size, low energy usage, and good scalability. We conduct a detailed comparison study of SRAM and MTJ BRAMs that includes cell designs that are robust with device variation, transistor-level design and optimization of all the required BRAM-specific circuits, and variation-aware simulation at the 22nm node. At a 256Kb block size, MTJ-BRAM is 3.06× denser and 55% more energy efficient and its F max is 274MHz, which is adequate for most FPGA system clock domains. We also detail further enhancements that allow these 256 Kb MTJ BRAMs to operate at a higher speed of 353MHz for the streaming FIFOs, which are very common in FPGA designs and describe how the non-volatility of MTJ BRAM enables novel on-chip configuration and power-down modes. For a RAM architecture similar to the latest commercial FPGAs, MTJ-BRAMs could expand FPGA memory capacity by 2.95× with no die size increase.

Funder

Connaught Scholarship, Toshiba

NSERC/Intel Industrial Research Chair in Programmable Silicon

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science

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1. CoMeFa: Deploying Compute-in-Memory on FPGAs for Deep Learning Acceleration;ACM Transactions on Reconfigurable Technology and Systems;2023-07-27

2. Dynamic power-gating for leakage power reduction in FPGAs;Frontiers of Information Technology & Electronic Engineering;2023-04

3. Large-scale combinatorial optimization in real-time systems by FPGA-based accelerators for simulated bifurcation;Proceedings of the 11th International Symposium on Highly Efficient Accelerators and Reconfigurable Technologies;2021-06-21

4. Compute-Capable Block RAMs for Efficient Deep Learning Acceleration on FPGAs;2021 IEEE 29th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM);2021-05

5. NVM-Based FPGA Block RAM With Adaptive SLC-MLC Conversion;IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems;2018-11

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