154,187 research outputs found
Structural Analysis: Shape Information via Points-To Computation
This paper introduces a new hybrid memory analysis, Structural Analysis,
which combines an expressive shape analysis style abstract domain with
efficient and simple points-to style transfer functions. Using data from
empirical studies on the runtime heap structures and the programmatic idioms
used in modern object-oriented languages we construct a heap analysis with the
following characteristics: (1) it can express a rich set of structural, shape,
and sharing properties which are not provided by a classic points-to analysis
and that are useful for optimization and error detection applications (2) it
uses efficient, weakly-updating, set-based transfer functions which enable the
analysis to be more robust and scalable than a shape analysis and (3) it can be
used as the basis for a scalable interprocedural analysis that produces precise
results in practice.
The analysis has been implemented for .Net bytecode and using this
implementation we evaluate both the runtime cost and the precision of the
results on a number of well known benchmarks and real world programs. Our
experimental evaluations show that the domain defined in this paper is capable
of precisely expressing the majority of the connectivity, shape, and sharing
properties that occur in practice and, despite the use of weak updates, the
static analysis is able to precisely approximate the ideal results. The
analysis is capable of analyzing large real-world programs (over 30K bytecodes)
in less than 65 seconds and using less than 130MB of memory. In summary this
work presents a new type of memory analysis that advances the state of the art
with respect to expressive power, precision, and scalability and represents a
new area of study on the relationships between and combination of concepts from
shape and points-to analyses
Supervised Learning in Spiking Neural Networks with Phase-Change Memory Synapses
Spiking neural networks (SNN) are artificial computational models that have
been inspired by the brain's ability to naturally encode and process
information in the time domain. The added temporal dimension is believed to
render them more computationally efficient than the conventional artificial
neural networks, though their full computational capabilities are yet to be
explored. Recently, computational memory architectures based on non-volatile
memory crossbar arrays have shown great promise to implement parallel
computations in artificial and spiking neural networks. In this work, we
experimentally demonstrate for the first time, the feasibility to realize
high-performance event-driven in-situ supervised learning systems using
nanoscale and stochastic phase-change synapses. Our SNN is trained to recognize
audio signals of alphabets encoded using spikes in the time domain and to
generate spike trains at precise time instances to represent the pixel
intensities of their corresponding images. Moreover, with a statistical model
capturing the experimental behavior of the devices, we investigate
architectural and systems-level solutions for improving the training and
inference performance of our computational memory-based system. Combining the
computational potential of supervised SNNs with the parallel compute power of
computational memory, the work paves the way for next-generation of efficient
brain-inspired systems
Neuromorphic In-Memory Computing Framework using Memtransistor Cross-bar based Support Vector Machines
This paper presents a novel framework for designing support vector machines
(SVMs), which does not impose restriction on the SVM kernel to be
positive-definite and allows the user to define memory constraint in terms of
fixed template vectors. This makes the framework scalable and enables its
implementation for low-power, high-density and memory constrained embedded
application. An efficient hardware implementation of the same is also
discussed, which utilizes novel low power memtransistor based cross-bar
architecture, and is robust to device mismatch and randomness. We used
memtransistor measurement data, and showed that the designed SVMs can achieve
classification accuracy comparable to traditional SVMs on both synthetic and
real-world benchmark datasets. This framework would be beneficial for design of
SVM based wake-up systems for internet of things (IoTs) and edge devices where
memtransistors can be used to optimize system's energy-efficiency and perform
in-memory matrix-vector multiplication (MVM).Comment: 4 pages, 5 figures, MWSCAS 201
RRAM variability and its mitigation schemes
Emerging technologies such as RRAMs are attracting significant attention due to their tempting characteristics such as high scalability, CMOS compatibility and non-volatility to replace the current conventional memories. However, critical causes of hardware reliability failures, such as process variation due to their nano-scale structure have gained considerable importance for acceptable memory yields. Such vulnerabilities make it essential to investigate new robust design strategies at the circuit system level. In this paper we have analyzed the RRAM variability phenomenon, its impact and variation tolerant techniques at the circuit level. Finally a variation-monitoring circuit is presented that discerns the reliable memory cells affected by process variability.Peer ReviewedPostprint (author's final draft
- …