Improving Memory Security and Reliability by Overcoming the Threat of Rowhammer

(Moin Qureshi, Professor of Computer Science at GA Tech, presenting on Wed. 10/26 at 1:00 & 7:00 PM ET.) 

Rowhammer allows an attacker to induce bit flips in a row by rapidly accessing neighboring rows. Rowhammer is not just a reliability concern but a severe security threat as it can be used to escalate privilege or break confidentiality. The problem of Rowhammer continues to become worse for two reasons: (1) The threshold of activations needed to induce Rowhammer reduces with each generation, coming down by 30x in the last 7 years (2) Attackers continue to come up with complex patterns that can break all hardware-based defenses, including the ones commercially employed in current chips. Currently, there is no guaranteed solution for Rowhammer. Hardware-based mitigation of Rowhammer typically consists of two parts: a tracker to identify aggressor rows and a mitigating action. At low thresholds tracking incurs significant SRAM overheads (several megabytes). Furthermore, the common mitigating action of refreshing neighboring victim rows is susceptible to the Half-Double attack from Google. In this talk, I will discuss our recent solutions that enable low-cost tracking of aggressor rows even at ultra-low thresholds (ISCA’22), a new mitigating action of performing dynamic row migration that is resilient to complex attacks patterns (ASPLOS’22 and MICRO’22), and a Rowhammer-aware ECC design that provides in-built memory integrity-protection while incurring virtually zero performance and storage overheads (HPCA’22).

 

Brief Bio: Moinuddin Qureshi is a Professor of Computer Science at the Georgia Institute of Technology. His research interests include computer architecture, hardware security, and quantum computing. Qureshi received his Ph.D. from the University of Texas at Austin in 2007. He was a research scientist at the IBM T. J. Watson Research Center (2007-2011), where he developed the caching algorithms for Power 7 Systems. Qureshi received the 2022 ACM SIGARCH Maurice Wilkes Award for contributions to high-performance memory systems. He is a member of Hall-of-Fame of the trifecta of architecture conferences: ISCA, MICRO, and HPCA. His research has been recognized with multiple best-paper awards and multiple IEEE Top-Picks awards. His papers were also awarded the 2019 NVMW Persistent Impact Prize and 2021 NVMW Persistent Impact Prize, in recognition of “exceptional impact on the fields of study related to non-volatile memories”. Qureshi received the 2020 “Outstanding Researcher Award” from Intel and an “Outstanding Technical Achievement” award from IBM Research. More information at https://www.cc.gatech.edu/~moin/

SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning

 Zihao Ye presenting on Wed. 10/19/22 at 1:00 & 7:00 PM ET

Sparse tensors are rapidly becoming critical components of modern deep learning workloads. However, developing high-performance sparse operators can be difficult and tedious, and existing vendor libraries cannot satisfy the escalating demands from new operators. Sparse tensor compilers simplify the development of operators, but efficient sparse compilation for deep learning remains challenging because a single sparse format cannot maximize hardware efficiency, and single-shot compilers cannot keep up with latest hardware and system advances. We show that the key to addressing both challenges is two forms of composability. In this paper, we propose SparseTIR, a sparse tensor compilation abstraction that offers composable formats and composable transformations for deep learning workloads. SparseTIR constructs a search space over these composable components for performance tuning. With these improvements, SparseTIR obtains consistent performance speedups vs vendor libraries and frameworks on sparse workloads such as Graph Neural Networks, Sparse Convolution, and Network Pruning.

MEMulator & PiMulator: Emerging Memory and Processing-in-Memory Architecture Emulation

(Sergiu Mosanu, UVA, presenting on October 12, 2022.)

Main memory is a crucial SoC and architecture design aspect, affecting system performance, power, and cost. The development of emerging memory technologies, increasingly specialized DRAM memory flavors, and Processing-in-Memory (PiM) architectures introduce the need for system-level modeling and evaluation. However, it is challenging to mimic both software and hardware aspects of emerging memory and PiM architectures using the currently available tools with high performance and fidelity. We develop a system emulation framework that employs a modular, parameterizable, FPGA synthesizable memory and PiM model. Implemented in System Verilog, the memory and PiM model allows users to generate any desired memory configuration on the FPGA fabric with complete control over the structure and distribution of the PiM logic units. We emulate a whole system by interfacing the memory emulation model with CPU soft cores and a soft memory controller. We demonstrate strategies to model several pioneering bitwise-PiM architectures and provide detailed benchmark performance results showing the platform's ability to facilitate design space exploration. We observe an emulation vs. simulation weighted-average speedup of 28x when running a memory benchmark workload. This comprehensive FPGA-based memory emulation enables fast, high-fidelity design-space exploration and evaluation of processing-in-memory architectures as part of a whole system stressed with heavy workloads.