218 research outputs found
Balanced Modulation for Nonvolatile Memories
This paper presents a practical writing/reading scheme in nonvolatile
memories, called balanced modulation, for minimizing the asymmetric component
of errors. The main idea is to encode data using a balanced error-correcting
code. When reading information from a block, it adjusts the reading threshold
such that the resulting word is also balanced or approximately balanced.
Balanced modulation has suboptimal performance for any cell-level distribution
and it can be easily implemented in the current systems of nonvolatile
memories. Furthermore, we studied the construction of balanced error-correcting
codes, in particular, balanced LDPC codes. It has very efficient encoding and
decoding algorithms, and it is more efficient than prior construction of
balanced error-correcting codes.Comment: 2 columns, 15 page
On the capacity of bounded rank modulation for flash memories
Rank modulation has been introduced as a new information representation scheme for flash memories. Given the charge levels of a group of flash cells, sorting is used to induce a permutation, which in turn represents data. Motivated by the lower sorting complexity of smaller cell groups, we consider bounded rank modulation, where a sequence of permutations of given sizes are used to represent data. We study the capacity of bounded rank modulation under the condition that permutations can overlap for higher capacity
Reliability Analysis of Nanocrystal Embedded High-k Nonvolatile Memories
The evolution of the MOSFET technology has been driven by the aggressive shrinkage of the device size to improve the device performance and to increase the circuit density. Currently, many research demonstrated that the continuous polycrystalline silicon film in the floating-gate dielectric could be replaced with nanocrystal (nc) embedded high-k thin film to minimize the charge loss due to the defective thin tunnel dielectric layer.
This research deals with both the statistical aspect of reliability and electrical aspect of reliability characterization as well. In this study, the Zr-doped HfO2 (ZrHfO) high-k MOS capacitors, which separately contain the nanocrystalline zinc oxide (nc-ZnO), silicon (nc-Si), Indium Tin Oxide (nc-ITO) and ruthenium (nc-Ru) are studied on their memory properties, charge transportation mechanism, ramp-relax test, accelerated life tests, failure rate estimation and thermal effect on the above reliability properties.
C-V hysteresis result show that the amount of charges trapped in nanocrystal embedded films is in the order of nc-ZnO\u3enc-Ru\u3enc-Si~nc-ITO, which might probably be influenced by the EOT of each sample. In addition, all the results show that the nc-ZnO embedded ZrHfO non-volatile memory capacitor has the best memory property and reliability. In this study, the optimal burn-in time for this kind of device has been also investigated with nonparametric Bayesian analysis. The results show the optimal burn-in period for nc-ZnO embedded high-k device is 5470s with the maximum one-year mission reliability
BinarEye: An Always-On Energy-Accuracy-Scalable Binary CNN Processor With All Memory On Chip in 28nm CMOS
This paper introduces BinarEye: a digital processor for always-on Binary
Convolutional Neural Networks. The chip maximizes data reuse through a Neuron
Array exploiting local weight Flip-Flops. It stores full network models and
feature maps and hence requires no off-chip bandwidth, which leads to a 230
1b-TOPS/W peak efficiency. Its 3 levels of flexibility - (a) weight
reconfiguration, (b) a programmable network depth and (c) a programmable
network width - allow trading energy for accuracy depending on the task's
requirements. BinarEye's full system input-to-label energy consumption ranges
from 14.4uJ/f for 86% CIFAR-10 and 98% owner recognition down to 0.92uJ/f for
94% face detection at up to 1700 frames per second. This is 3-12-70x more
efficient than the state-of-the-art at on-par accuracy.Comment: Presented at the 2018 IEEE Custom Integrated Circuits Conference
(CICC). Presentation is available here:
https://www.researchgate.net/publication/324452819_Presentation_on_Binareye_at_CIC
Phase change memory technology
We survey the current state of phase change memory (PCM), a non-volatile
solid-state memory technology built around the large electrical contrast
between the highly-resistive amorphous and highly-conductive crystalline states
in so-called phase change materials. PCM technology has made rapid progress in
a short time, having passed older technologies in terms of both sophisticated
demonstrations of scaling to small device dimensions, as well as integrated
large-array demonstrators with impressive retention, endurance, performance and
yield characteristics.
We introduce the physics behind PCM technology, assess how its
characteristics match up with various potential applications across the
memory-storage hierarchy, and discuss its strengths including scalability and
rapid switching speed. We then address challenges for the technology, including
the design of PCM cells for low RESET current, the need to control
device-to-device variability, and undesirable changes in the phase change
material that can be induced by the fabrication procedure. We then turn to
issues related to operation of PCM devices, including retention,
device-to-device thermal crosstalk, endurance, and bias-polarity effects.
Several factors that can be expected to enhance PCM in the future are
addressed, including Multi-Level Cell technology for PCM (which offers higher
density through the use of intermediate resistance states), the role of coding,
and possible routes to an ultra-high density PCM technology.Comment: Review articl
Energy-Efficient Streaming Using Non-volatile Memory
The disk and the DRAM in a typical mobile system consume a significant fraction (up to 30%) of the total system energy. To save on storage energy, the DRAM should be small and the disk should be spun down for long periods of time. We show that this can be achieved for predominantly streaming workloads by connecting the disk to the DRAM via a large non-volatile memory (NVM). We refer to this as the NVM-based architecture (NVMBA); the conventional architecture with only a DRAM and a disk is referred to as DRAMBA. The NVM in the NVMBA acts as a traffic reshaper from the disk to the DRAM. The total system costs are balanced, since the cost increase due to adding the NVM is compensated by the decrease in DRAM cost. We analyze the energy saving of NVMBA, with NAND flash memory serving as NVM, relative to DRAMBA with respect to (1) the streaming demand, (2) the disk form factor, (3) the best-effort provision, and (4) the stream location on the disk. We present a worst-case analysis of the reliability of the disk drive and the flash memory, and show that a small flash capacity is sufficient to operate the system over a year at negligible cost. Disk lifetime is superior to flash, so that is of no concern
In-memory computing with emerging memory devices: Status and outlook
Supporting data for "In-memory computing with emerging memory devices: status and outlook", submitted to APL Machine Learning
Exploiting Data Longevity for Enhancing the Lifetime of Flash-based Storage Class Memory
Storage-class memory (SCM) combines the benefits of a solid-state memory,
such as high-performance and robustness, with the archival capabilities and low
cost of conventional hard-disk magnetic storage. Among candidate solid-state
nonvolatile memory technologies that could potentially be used to construct
SCM, flash memory is a well-established technology and have been widely used in
commercially available SCM incarnations. Flash-based SCM enables much better
tradeoffs between performance, space and power than disk-based systems.
However, write endurance is a significant challenge for a flash-based SCM (each
act of writing a bit may slightly damage a cell, so one flash cell can be
written 10^4--10^5 times, depending on the flash technology, before it becomes
unusable). This is a well-documented problem and has received a lot of
attention by manufactures that are using some combination of write reduction
and wear-leveling techniques for achieving longer lifetime. In an effort to
improve flash lifetime, first, by quantifying data longevity in an SCM, we show
that a majority of the data stored in a solid-state SCM do not require long
retention times provided by flash memory (i.e., up to 10 years in modern
devices); second, by exploiting retention time relaxation, we propose a novel
mechanism, called Dense-SLC (D-SLC), which enables us perform multiple writes
into a cell during each erase cycle for lifetime extension; and finally, we
discuss the required changes in the flash management software (FTL) in order to
use this characteristic for extending the lifetime of the solid-state part of
an SCM. Using an extensive simulation-based analysis of a flash-based SCM, we
demonstrate that D-SLC is able to significantly improve device lifetime
(between 5.1X and 8.6X) with no performance overhead and also very small
changes at the FTL software
Reliable and Energy Efficient MLC STT-RAM Buffer for CNN Accelerators
We propose a lightweight scheme where the formation of a data block is changed in such a way that it can tolerate soft errors significantly better than the baseline. The key insight behind our work is that CNN weights are normalized between -1 and 1 after each convolutional layer, and this leaves one bit unused in half-precision floating-point representation. By taking advantage of the unused bit, we create a backup for the most significant bit to protect it against the soft errors. Also, considering the fact that in MLC STT-RAMs the cost of memory operations (read and write), and reliability of a cell are content-dependent (some patterns take larger current and longer time, while they are more susceptible to soft error), we rearrange the data block to minimize the number of costly bit patterns. Combining these two techniques provides the same level of accuracy compared to an error-free baseline while improving the read and write energy by 9% and 6%, respectively
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