Migration and Business Cycles Selective Immigration Get access Cycles. By Harry Jerome, with a foreword by Wesley C. Mitchell. 1926. (New York: National Bureau of Economic Research, Inc. 8vo. 256 pp.)Selective Immigration. James J. Davis, Secretary Labour, 1925. (St. Paul, Minnesota: Scott-Mitchell Publishing Co. 227 pp.) D. Tait Search for other works this author on: Oxford Academic Google Scholar Journal the Royal Institute International Affairs, Volume 7, Issue 2, March 1928, Pages...

Research has shown that deep neural networks contain significant redundancy, and high classification accuracies can be achieved even when weights activations are quantised down to binary values. Network binarisation on FPGAs greatly increases area efficiency by replacing resource-hungry multipliers with lightweight XNOR gates. However, an FPGA's fundamental building block, the K-LUT, is capable of implementing far more than XNOR: it perform any K-input Boolean operation. Inspired this...

Modern embedded systems consist of heterogeneous computing resources with diverse energy and performance trade-offs. This is because these exercise the application tasks differently, generating varying workloads consumption. As a result, minimizing consumption in challenging as continuous adaptation between task mapping (i.e. allocating among resources) dynamic voltage/frequency scaling (DVFS) required. Existing approaches have limitations due to lack such practical validation (Table I)....

Research has shown that deep neural networks contain significant redundancy, and thus high classification accuracy can be achieved even when weights activations are quantized down to binary values. Network binarization on FPGAs greatly increases area efficiency by replacing resource-hungry multipliers with lightweight XNOR gates. However, an FPGA's fundamental building block, the K-LUT, is capable of implementing far more than XNOR: it perform any K-input Boolean operation. Inspired this...

This work presents a self-contained and modifiable framework for fast easy convolutional neural network prototyping on the Xilinx PYNQ platform. With Python-based programming interface, combines convenience of high-level abstraction with speed optimised FPGA implementation. Our is freely available GitHub community to use build upon.

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ABSTRACT The purpose of the study was to assess attitudes first‐ and second‐year foreign language students enrolled at historically predominantly Black colleges universities. Specifically, researchers assessed student regarding in general toward linguistic cultural component instruction. A secondary gather information from faculty administrators concerning current status programs cooperating institutions. total 53, or 70% 76 targeted institutions, participated its entirety. Fifty‐seven (75...

Deploying a deep neural network model on reconfigurable platform, such as an FPGA, is challenging due to the enormous design spaces of both models and hardware design. A has various layer types, connection patterns data representations, corresponding implementation can be customised with different architectural modular parameters. Rather than manually exploring this space, it more effective automate optimisation throughout end-to-end compilation process. This paper provides overview recent...

Biological datasets amenable to applied machine learning are more available today than ever before, yet they lack adequate representation in the Data-for-Good community. Here we present a work progress case study performing analysis on antimicrobial resistance (AMR) using standard ensemble techniques and note successes pitfalls such entails. Broadly, (AML) well suited AMR, with classification accuracies ranging from mid-90% low- 80% depending sample size. Additionally, these prove successful...

This article studies the interplay between performance, energy, and reliability (PER) of parallel-computing systems. It describes methods supporting meaningful cross-platform analysis this interplay. These lead to PER software tool, which helps designers analyze, compare, explore these properties. The web extra at https://youtu.be/aijVMM3Klfc illustrates (performance, reliability) expanding on main engineering principles described in article.

In an FPGA system-on-chip design, it is often insufficient to merely assess the power consumption of entire circuit by compile-time estimation or runtime measurement. Instead, make better decisions, one must understand consumed each module in system. this work, we combine measurements register-level switching activity and system-level build adaptive online model that produces live breakdowns within design. Online refinement avoids time-consuming characterization while also allowing track...

In a modern FPGA system-on-chip design, it is often insufficient to simply assess the total power consumption of entire circuit by design-time estimation or runtime rail measurement. Instead, make better decisions, desirable understand consumed each individual module in system. this work, we combine board-level measurements with register-level activity counting build an online model that produces breakdown within design. Online refinement avoids need for time-consuming characterisation stage...

Many algorithms feature an iterative loop that converges to the result of interest. The numerical operations in such are generally implemented using finite-precision arithmetic, either fixed- or floating-point, most which operate least-significant digit first. This results a fundamental problem: if, after some time, has not converged, is this because we have run algorithm for enough iterations arithmetic was insufficiently precise? There no easy way answer question, so users will often...

While allowing for the fabrication of increasingly complex and efficient circuitry, transistor shrinkage count-per-device expansion have major downsides: chiefly increased variation, degradation fault susceptibility. For this reason, design-time consideration tolerance will to be given increasing numbers electronic systems in future ensure yields, reliabilities lifetimes remain acceptably high. Many commonly implemented operators are suited modification resulting datapath error detection...

FPGA-specific DNN architectures using the native LUTs as independently trainable inference operators have been shown to achieve favorable area-accuracy and energy-accuracy tradeoffs. The first work in this area, LUTNet, exhibited state-of-the-art performance for standard benchmarks. In paper, we propose learned optimization of such LUT-based topologies, resulting higher-efficiency designs than via direct use off-the-shelf, hand-designed networks. Existing implementations class architecture...

The ever-growing computational demands of increasingly complex machine learning models frequently necessitate the use powerful cloud-based infrastructure for their training. Binary neural networks are known to be promising candidates on-device inference due extreme compute and memory savings over higher-precision alternatives. However, existing training methods require concurrent storage high-precision activations all layers, generally making on memory-constrained devices infeasible. In this...

Research has shown that deep neural networks contain significant redundancy, and high classification accuracies can be achieved even when weights activations are quantised down to binary values. Network binarisation on FPGAs greatly increases area efficiency by replacing resource-hungry multipliers with lightweight XNOR gates. However, an FPGA's fundamental building block, the K-LUT, is capable of implementing far more than XNOR: it perform any K-input Boolean operation. Inspired this...

While we reap the benefits of process scaling in terms transistor density and switching speed, consideration must be given to negative effects it causes: increased variation, degradation fault susceptibility. Above device level, such phenomena faults they induce can lead reduced yield, decreased system reliability and, extreme cases, total failure after a period successful operation. Although error detection correction are almost always considered for highly sensitive susceptible...

Many algorithms feature an iterative loop that converges to the result of interest. The numerical operations in such are generally implemented using finite-precision arithmetic, either fixed or floating point, most which operate least-significant digit first. This results a fundamental problem: if, after some time, has not converged, is this because we have run algorithm for enough iterations arithmetic was insufficiently precise? There no easy way answer question, so users will often...