Hydra: An Accelerator for Real-Time Edge-Aware Permeability Filtering in 65nm CMOS

Many modern video processing pipelines rely on edge-aware (EA) filtering methods. However, recent high-quality methods are challenging to run in real-time on embedded hardware due to their computational load. To this end, we propose an area-efficient and real-time capable hardware implementation of a high quality EA method. In particular, we focus on the recently proposed permeability filter (PF) that delivers promising quality and performance in the domains of HDR tone mapping, disparity and optical flow estimation. We present an efficient hardware accelerator that implements a tiled variant of the PF with low on-chip memory requirements and a significantly reduced external memory bandwidth (6.4x w.r.t. the non-tiled PF). The design has been taped out in 65 nm CMOS technology, is able to filter 720p grayscale video at 24.8 Hz and achieves a high compute density of 6.7 GFLOPS/mm2 (12x higher than embedded GPUs when scaled to the same technology node). The low area and bandwidth requirements make the accelerator highly suitable for integration into SoCs where silicon area budget is constrained and external memory is typically a heavily contended resource

Endpoint Cluster Identification for End-to-End Distance Estimation,” ICC’06

Abstract-Distributed systems such as peer-to-peer networks and distributed servers can optimize their performance by adapting to the underlying network. End-to-end measurements are an important basis for such adaptivity. Although most applications measure similar properties of the network, the measurements are mostly done in application-specific ways. In this paper we propose a general peer-to-peer measurement service based on clusters of endpoints that show virtually identical QoS properties when observed from outside the cluster. We discuss the clustering concept as well as its use in the measurement service, and we present a measurement-based method for the remote identification of clusters. This method allows for detecting clusters that are not part of the peer-to-peer network. Our evaluation shows that the presented method is able to reliably detect clusters using measurements of round-trip time or of available bandwidth

FloatX: A C++ Library for Customized Floating-Point Arithmetic

"© ACM, 2019. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM Transactions on Mathematical Software, {45, 4, (2019)} https://dl.acm.org/doi/10.1145/3368086"[EN] We present FloatX (Float eXtended), a C++ framework to investigate the effect of leveraging customized floating-point formats in numerical applications. FloatX formats are based on binary IEEE 754 with smaller significand and exponent bit counts specified by the user. Among other properties, FloatX facilitates an incremental transformation of the code, relies on hardware-supported floating-point types as back-end to preserve efficiency, and incurs no storage overhead. The article discusses in detail the design principles, programming interface, and datatype casting rules behind FloatX. Furthermore, it demonstrates FloatX's usage and benefits via several case studies from well-known numerical dense linear algebra libraries, such as BLAS and LAPACK; the Ginkgo library for sparse linear systems; and two neural network applications related with image processing and text recognition.This work was supported by the CICYT projects TIN2014-53495-R and TIN2017-82972-R of the MINECO and FEDER, and the EU H2020 project 732631 "OPRECOMP. Open Transprecision Computing."Flegar, G.; Scheidegger, F.; Novakovic, V.; Mariani, G.; Tomás Domínguez, AE.; Malossi, C.; Quintana-Ortí, ES. (2019). FloatX: A C++ Library for Customized Floating-Point Arithmetic. ACM Transactions on Mathematical Software. 45(4):1-23. https://doi.org/10.1145/3368086S123454Edward Anderson Zhaojun Bai L. Susan Blackford James Demmesl Jack J. Dongarra Jeremy Du Croz Sven Hammarling Anne Greenbaum Alan McKenney and Danny C. Sorensen. 1999. LAPACK Users’ Guide (3rd ed.). SIAM. Edward Anderson Zhaojun Bai L. 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Rethinking numerical representations for deep neural networks. arXiv e-prints (Aug 2018). arXiv:1808.02513. Retrieved from https://openreview.net/forum?id&equals;BJ_MGwqlg8noteId&equals;BJ_MGwqlg.Parker Hill Babak Zamirai Shengshuo Lu Yu-Wei Chao Michael Laurenzano Mehrzad Samadi Marios Papaefthymiou Scott Mahlke Thomas Wenisch Jia Deng etal 2018. Rethinking numerical representations for deep neural networks. 2018. Parker Hill Babak Zamirai Shengshuo Lu Yu-Wei Chao Michael Laurenzano Mehrzad Samadi Marios Papaefthymiou Scott Mahlke Thomas Wenisch Jia Deng et al. 2018. Rethinking numerical representations for deep neural networks. 2018.IBM. 2015. Engineering and Scientific Subroutine Library. Retrieved from http://www-03.ibm.com/systems/power/software/essl/. IBM. 2015. Engineering and Scientific Subroutine Library. Retrieved from http://www-03.ibm.com/systems/power/software/essl/.IEEE. 2008. IEEE Standard for Floating-point Arithmetic. IEEE Std 754-2008 (Aug. 2008) 1--70. DOI:https://doi.org/10.1109/IEEESTD.2008.4610935 IEEE. 2008. IEEE Standard for Floating-point Arithmetic. IEEE Std 754-2008 (Aug. 2008) 1--70. DOI:https://doi.org/10.1109/IEEESTD.2008.4610935Intel. 2015. Math Kernel Library. Retrieved from https://software.intel.com/en-us/intel-mkl. Intel. 2015. Math Kernel Library. Retrieved from https://software.intel.com/en-us/intel-mkl.ISO. 2017. ISO International Standard ISO/IEC 14882:2017(E)—Programming Language C++. Retrieved from https://isocpp.org/std/the-standard. Visited June 2018. ISO. 2017. ISO International Standard ISO/IEC 14882:2017(E)—Programming Language C++. Retrieved from https://isocpp.org/std/the-standard. Visited June 2018.Lefevre, V. (2013). SIPE: Small Integer Plus Exponent. 2013 IEEE 21st Symposium on Computer Arithmetic. doi:10.1109/arith.2013.22Liu, Z., Luo, P., Wang, X., & Tang, X. (2015). Deep Learning Face Attributes in the Wild. 2015 IEEE International Conference on Computer Vision (ICCV). doi:10.1109/iccv.2015.425Érik Martin-Dorel Guillaume Melquiond and Jean-Michel Muller. 2013. Some issues related to double rounding. BIT Num. Math. 53 4 (01 Dec. 2013) 897--924. DOI:https://doi.org/10.1007/s10543-013-0436-2 Érik Martin-Dorel Guillaume Melquiond and Jean-Michel Muller. 2013. Some issues related to double rounding. BIT Num. Math. 53 4 (01 Dec. 2013) 897--924. DOI:https://doi.org/10.1007/s10543-013-0436-2Sparsh Mittal. 2016. A survey of techniques for approximate computing. ACM Comput. Surv. 48 4 Article 62 (Mar. 2016) 33 pages. DOI:https://doi.org/10.1145/2893356 Sparsh Mittal. 2016. A survey of techniques for approximate computing. ACM Comput. Surv. 48 4 Article 62 (Mar. 2016) 33 pages. DOI:https://doi.org/10.1145/2893356NVIDIA. 2016. cuBLAS. Retrieved from https://developer.nvidia.com/cublas. NVIDIA. 2016. cuBLAS. 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The concept of transprecision computing

For many years, computing systems rely on guaranteed numerical precision of each step in complex computations. Moore's law sustains exponential improvements in the semiconductor industry over several decades for building computing infrastructure, from tiny Internet-of-Things nodes, over personal smartphones, laptops or workstations, up to large HPC computing server centers. With the paradigm of the ''power wall'', achievable improvements start to saturate. To that end, the concept of transprecision computing emerged, where existing over-conservative ''precis'' computing assumptions are relaxed and replaced with more flexible and efficient policies to gain performance. Unfortunately, it is non-straight forward to adopt and integrate general transprecision concepts into the variety of today's computing infrastructure. The main challenge consists of leveraging domain-specific knowledge and provide full solutions covering from physical foundations over circuit-level up through the full software stack to the application level. This work focuses on how transprecision concepts improve general computing. We identify and elaborate the standard number representations, especially the one defined in the IEEE 754 floating-point standard, as the enabler of low precision computing. We developed lightweight libraries that allow integrating transprecision concepts into algorithms. Finally, we focus on building automatized workflows for specific problems, where the solution space is enlarged by multiple orders of magnitude due to the various configurations of low precision. We demonstrate how heuristic optimization strategies applied on top of transprecision computing find near to optimal configurations of approximated kernels in a short time

Schmetterlinge

Schmetterlinge gehören zu den bekanntesten Insekten. Es gibt eine grosse Vielfalt an Arten. In der Umgangssprache werden die Schmetterlinge in Tagfalter und Nachtfalter (oder «Motten») eingeteilt. Es gibt in der Schweiz 210 verschiedene Tagfalterarten. Ihnen stehen rund 3460 Arten an «Nachtfaltern» gegenüber. Die meisten dieser Arten sind tatsächlich nachtaktiv, es gibt aber eine kleine Gruppe «Nachtfalter», welche auch am Tag zu finden sind (Widderchen sind ebenfalls tagaktiv). Tagfalter haben kolbenförmig verdickte Fühler und stellen die Flügel in Ruheposition senkrecht über dem Körper auf. Nachtfalter haben meist fadenartige oder kammartige Fühler. Sie falten ihre Flügel in der Ruhestellung typischerweise rückwärts über den Körper und fallen, beispielsweise auf einem Baumstamm sitzend, tagsüber kaum auf. Tagaktive Arten kann man auch mit einem Fernglas beobachten. Nachtaktive Arten entziehen sich mehr der Beobachtung. Weil die meisten Arten vom Licht angelockt werden, können sie an einem Lichtturm beobachtet und bestimmt werden. Einige Arten muss man zwecks Bestimmung einfangen. Fast alle Schmetterlinge sind auf bestimmte Lebensräume angewiesen, und sie können dadurch als Indikatorarten verwendet werden. Am Pfäffikersee leben viele Schmetterlingsarten, darunter auch sehr seltene und bedrohte, welche auf Feuchtgebiete angewiesen sind. Wir fanden bei den Tagfaltern vor allem Arten, welche auf Hochstaudenfluren, Streuwiesen und Flachmoore spezialisiert sind. Bei den Nachtfaltern gibt es auch einige auf Hochmoore spezialisierte Arten