1,564 research outputs found
An Experimental Study of Reduced-Voltage Operation in Modern FPGAs for Neural Network Acceleration
We empirically evaluate an undervolting technique, i.e., underscaling the
circuit supply voltage below the nominal level, to improve the power-efficiency
of Convolutional Neural Network (CNN) accelerators mapped to Field Programmable
Gate Arrays (FPGAs). Undervolting below a safe voltage level can lead to timing
faults due to excessive circuit latency increase. We evaluate the
reliability-power trade-off for such accelerators. Specifically, we
experimentally study the reduced-voltage operation of multiple components of
real FPGAs, characterize the corresponding reliability behavior of CNN
accelerators, propose techniques to minimize the drawbacks of reduced-voltage
operation, and combine undervolting with architectural CNN optimization
techniques, i.e., quantization and pruning. We investigate the effect of
environmental temperature on the reliability-power trade-off of such
accelerators. We perform experiments on three identical samples of modern
Xilinx ZCU102 FPGA platforms with five state-of-the-art image classification
CNN benchmarks. This approach allows us to study the effects of our
undervolting technique for both software and hardware variability. We achieve
more than 3X power-efficiency (GOPs/W) gain via undervolting. 2.6X of this gain
is the result of eliminating the voltage guardband region, i.e., the safe
voltage region below the nominal level that is set by FPGA vendor to ensure
correct functionality in worst-case environmental and circuit conditions. 43%
of the power-efficiency gain is due to further undervolting below the
guardband, which comes at the cost of accuracy loss in the CNN accelerator. We
evaluate an effective frequency underscaling technique that prevents this
accuracy loss, and find that it reduces the power-efficiency gain from 43% to
25%.Comment: To appear at the DSN 2020 conferenc
Strategies to enhance the 3T1D-DRAM cell variability robustness beyond 22 nm
3T1D cell has been stated as a valid alternative to be implemented on L1 memory cache to substitute 6T, highly affected by device variability as technology dimensions are reduced. In this work, we have shown that 22 nm 3T1D memory cells present significant tolerance to high levels of device parameter fluctuation. Moreover, we have observed that when variability is considered the write access transistor becomes a significant detrimental element on the 3T1D cell performance. Furthermore, resizing and temperature control have been presented as some valid strategies in order to mitigate the 3T1D cell variability.Peer ReviewedPostprint (author's final draft
RAMPART: RowHammer Mitigation and Repair for Server Memory Systems
RowHammer attacks are a growing security and reliability concern for DRAMs
and computer systems as they can induce many bit errors that overwhelm error
detection and correction capabilities. System-level solutions are needed as
process technology and circuit improvements alone are unlikely to provide
complete protection against RowHammer attacks in the future. This paper
introduces RAMPART, a novel approach to mitigating RowHammer attacks and
improving server memory system reliability by remapping addresses in each DRAM
in a way that confines RowHammer bit flips to a single device for any victim
row address. When RAMPART is paired with Single Device Data Correction (SDDC)
and patrol scrub, error detection and correction methods in use today, the
system can detect and correct bit flips from a successful attack, allowing the
memory system to heal itself. RAMPART is compatible with DDR5 RowHammer
mitigation features, as well as a wide variety of algorithmic and probabilistic
tracking methods. We also introduce BRC-VL, a variation of DDR5 Bounded Refresh
Configuration (BRC) that improves system performance by reducing mitigation
overhead and show that it works well with probabilistic sampling methods to
combat traditional and victim-focused mitigation attacks like Half-Double. The
combination of RAMPART, SDDC, and scrubbing enables stronger RowHammer
resistance by correcting bit flips from one successful attack. Uncorrectable
errors are much less likely, requiring two successful attacks before the memory
system is scrubbed.Comment: 16 pages, 13 figures. A version of this paper will appear in the
Proceedings of MEMSYS2
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