SAFARI Project & Seminars Courses (Spring 2023)

DRAM is predominantly used to build the main memory systems of modern computing devices. To improve the performance, reliability, and security of DRAM, it is critical to perform experimental characterization and analysis of existing cutting-edge DRAM chips.

DRAM Bender (a.k.a. SoftMCv2) is an FPGA-based DRAM testing infrastructure that enables the programmer to perform all low-level DRAM operations (i.e., DDR commands) in a cycle-accurate manner. DRAM Bender provides a simple and intuitive high-level programming interface (in C++) that completely hides the low-level details of the FPGA from programmers. Programmers implement test routines in C++, and the test routines automatically get translated into the low-level DRAM Bender instructions in the FPGA. DRAM Bender hardware developers write low-level hardware description language code to enable new and faster studies.

In this P&S, you will have the chance to learn how DRAM is organized and operates in a low-level and gain practical experience in using DRAM Bender while developing DRAM test programs for new DRAM characterization studies related to performance, reliability, and security. You may also improve the DRAM Bender infrastructure itself to enable new studies. And, who knows, you might discover new security vulnerabilities like RowHammer.

This will be the right P&S for you if you are interested in DRAM technology and would like to learn more about it as well as FPGA technology and how it can be used for practical purposes such as understanding and mitigating RowHammer attacks, generating true random numbers, reducing memory latency, fingerprinting and identifying devices, and improving reliability.

Prerequisites of the course:

• Digital Design and Computer Architecture (or equivalent course)
• Familiarity with FPGA programming
• Interest in low-level system exploration and memory
• Interest in discovering why things do or do not work and solving problems

The course is conducted in English.

Course description page Moodle

Mailing List: safari-ps-softmc@sympa.ethz.ch (sent to all mentors)

Lecture Playlist

• DRAMBender Tutorial Video: https://www.youtube.com/watch?v=FklVEsfdZCI
• SoftMC lecture: https://www.youtube.com/watch?v=tnSPEP3t-Ys
• Paper describing SoftMC: https://people.inf.ethz.ch/omutlu/pub/softMC_hpca17.pdf
• Paper describing DRAMBender: https://arxiv.org/abs/2211.05838
• Example RowHammer study using SoftMC: https://people.inf.ethz.ch/omutlu/pub/Revisiting-RowHammer_isca20.pdf

• Example security attack study using SoftMC: https://people.inf.ethz.ch/omutlu/pub/rowhammer-TRRespass_ieee_security_privacy20.pdf
• Example neural network acceleration study using SoftMC: https://people.inf.ethz.ch/omutlu/pub/EDEN-efficient-DNN-inference-with-approximate-memory_micro19.pdf
• Example random number generation study using SoftMC: https://people.inf.ethz.ch/omutlu/pub/drange-dram-latency-based-true-random-number-generator_hpca19.pdf
• Example physical unclonable function study using SoftMC: https://people.inf.ethz.ch/omutlu/pub/dram-latency-puf_hpca18.pdf
• The original RowHammer study using SoftMC: https://people.inf.ethz.ch/omutlu/pub/dram-row-hammer_isca14.pdf

• An old version of SoftMC is here: https://github.com/CMU-SAFARI/SoftMC
• A recent version of DRAMBender is here: https://github.com/CMU-SAFARI/DRAM-Bender

• HW0 handout