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Showing 1 to 20 of 28 for “"Approximate Computing"”.
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Memory-centric approximate computing
Made available in DSpace on 2020-08-26T23:58:45Z (GMT). No. of bitstreams: 2 WANG-THESIS-2020.pdf: 767488 bytes, checksum: f279727e3637c38ea03e13ef0926d1a7 (MD5) LICENSE.txt: 4210 bytes, checksum: b0afabfe300d734e5c194fcc53c0a560 (MD5) Previous issue date: 2020-05-12
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Approximate computing: An integrated cross-layer framework
<p>A new design approach, called <em>approximate computing</em> (AxC), leverages the flexibility provided by intrinsic application resilience to realize hardware or software implementations that are more efficient in energy or performance. Approximate computing techniques forsake exact (numerical …
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Towards accessible, trustworthy high-performance approximate computing
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-20 without embargo terms
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Software-based approximate computing for mathematical functions
… makes original contributions to the area of approximate computing. We demonstrate new approaches to safe-approximation and justify approximate computation generally by showing that existing mathematical libraries are already suffering the downsides of approximation and latent error without …
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Approximate computing techniques for accelerating compute intensive workloads
High Performance Computing involves improving the computational performance of memory and compute intensive workloads in science or engineering. One of the main components to the current success of ML is the ability to perform computations on very large amounts of training data. Similarly in the …
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Compilers for portable programming of heterogeneous parallel & approximate computing systems
… is further complicated by software and hardware approximate computing optimizations. Different compute units on an SoC use different approximate computing methods and an application would usually be composed of multiple compute kernels, each one specialized to run on a different hardware. …
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BROAD: bold and reliable online approximate computing framework for diverse applications
Approximate computing is an emerging computing paradigm that leverages the inherent resilience of applications while designing energy-efficient computing systems. Approximate computing systems must satisfy user-provided requirements for quality of service (QoS), a quantitative criterion imposed on …
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Reliable, secure and energy-efficient AI hardware
… To date, many energy-aware solutions such as approximate computing have been proposed to address the energy constraints of AI devices. Approximate computing-based deep learning algorithms relax the abstraction with near-perfect accuracy for energy efficiency in errorresilient applications. …
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Energy-efficient approximate computation in Topaz
… as a first-order concern in contemporary computing systems has motivated the design of energy-efficient approximate computing platforms. These computing platforms feature energy-efficient computing mechanisms such as components that may occasionally produce incorrect results. We present …
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Implicit Programming and Formal Pragmatics
… approach is sufficiently general to encompass approximate computing and probabilistic programming within a single framework. We then focus on its application in approximate computing and build a particular intent-specific programming language, FAST, to show how it allows users to code a variety …
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Software-defined Significance-Driven Computing
Approximate computing has been an emerging programming and system design paradigm that has been proposed as a way to overcome the <br/>power-wall problem that hinders the scaling of the next generation of both high-end and mobile computing systems. Towards this<br/>end, a lot of researchers have …
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Energy efficient computing exploiting data similarity and computation redundancy
… fields, such as machine learning, scientific computing and signal/image processing, need to deal with real-world input datasets. Such input datasets are usually discrete samples of slow-changing, continuous data of physical phenomena, like temperature maps and images. Due to the continuous …
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Cross-layer instruction-aware timing error mitigation & evaluation for energy-efficient dependable architectures
… At application/software-layer, the concept of approximate computing is leveraged to minimise timing errors. In the second part, two accurate timing error modeling and evaluation frameworks are proposed; for the first time the instruction execution history (i.e., type and order of instructions …
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Exploiting application level error resilience via deferred execution
Many programs exhibit application level error resilience which allows certain subcomputations to execute in an imprecise, yet energy efficient manner, potentially yielding significant overall energy savings without sacrificing end- to-end quality. In this thesis we identify one fundamental problem …
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Democratizing error-efficient computing
We live in a world where errors in computing are becoming ubiquitous and come from a wide variety of sources -- from unintentional bit flips in devices to deliberate approximations and malicious attacks. Future systems must be built to extract maximum computational efficiency while operating with …
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Exploring Per-Input Filter Selection and Approximation Techniques for Deep Neural Networks
… trained full precision filter weights and the approximated weights, a metric called Multiplication Error (ME) has been chosen. For convolutional layers, ME is calculated by subtracting the approximated filter weights from the original filter weights, convolving the difference with the input and …
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ApproxHPVM: A retargetable compiler framework for accuracy-aware optimizations
… computational burden on low-end edge devices. Approximate computing can help bridge the gap between increasing computational demands and limited compute power on such devices. We present ApproxHPVM, a portable optimizing compiler and runtime system that enables flexible, optimized use of …
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Secure, Resilient and Low-Energy Hardware Architectures for Internet-of-Things
… which require high energy. A technique termed approximate computing is applied to decrease the energy consumption of systems. Specically, applying bit-width reduction to modify architectures to lower energy consumption by incrementally increasing the precision in stages and using multi-level …
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Efficient machine learning: models and accelerations
… address these two problems and utilize different computing paradigms to solve real-life deep learning problems.</p> <p>To explore in these two domains, this thesis first presents the cogent confabulation network for sentence completion problem. We use Chinese language as a case study to describe …
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Statistical error compensation for robust digital signal processing and machine learning
… is explored as well. Recent studies on approximate computing (AC) follow a principle similar to SEC, but with one critical exception. AC based design still carries the requirement of creating a deterministic design, and thus the improvement in energy efficiency is marginal. We …
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