Incremental Learning from Scratch for Task-Oriented Dialogue Systems

Clarifying user needs is essential for existing task-oriented dialogue systems. However, in real-world applications, developers can never guarantee that all possible user demands are taken into account in the design phase. Consequently, existing systems will break down when encountering unconsidered user needs. To address this problem, we propose a novel incremental learning framework to design task-oriented dialogue systems, or for short Incremental Dialogue System (IDS), without pre-defining the exhaustive list of user needs. Specifically, we introduce an uncertainty estimation module to evaluate the confidence of giving correct responses. If there is high confidence, IDS will provide responses to users. Otherwise, humans will be involved in the dialogue process, and IDS can learn from human intervention through an online learning module. To evaluate our method, we propose a new dataset which simulates unanticipated user needs in the deployment stage. Experiments show that IDS is robust to unconsidered user actions, and can update itself online by smartly selecting only the most effective training data, and hence attains better performance with less annotation cost.Comment: ACL201

A Survey on Approximate Multiplier Designs for Energy Efficiency: From Algorithms to Circuits

Given the stringent requirements of energy efficiency for Internet-of-Things edge devices, approximate multipliers, as a basic component of many processors and accelerators, have been constantly proposed and studied for decades, especially in error-resilient applications. The computation error and energy efficiency largely depend on how and where the approximation is introduced into a design. Thus, this article aims to provide a comprehensive review of the approximation techniques in multiplier designs ranging from algorithms and architectures to circuits. We have implemented representative approximate multiplier designs in each category to understand the impact of the design techniques on accuracy and efficiency. The designs can then be effectively deployed in high-level applications, such as machine learning, to gain energy efficiency at the cost of slight accuracy loss.Comment: 38 pages, 37 figure

A reconfigurable stochastic architecture for highly reliable computing

Mounting concerns over variability, defects and noise motivate a new approach for integrated circuits: the design of stochastic logic, that is to say, digital circuitry that operates on probabilistic signals, and so can cope with errors and uncertainty. Techniques for prob-abilistic analysis are well established. We advocate a strategy for synthesis. In this paper, we present a reconfigurable architecture that implements the computation of arbitrary continuous functions with stochastic logic. We analyze the sources of error: approxima-tion, quantization, and random fluctuations. We demonstrate the ef-fectiveness of our method on a collection of benchmarks for image processing. Synthesis trials show that our stochastic architecture requires less area than conventional hardware implementations. It achieves a large speed up compared to software conventional im-plementations. Most importantly, it is much more tolerant of soft errors (bit flips) than these deterministic implementations

Time-Reversal-Even Nonlinear Current Induced Spin Polarization

We propose a time-reversal-even spin generation in second order of electric fields, which dominates the current induced spin polarization in a wide class of centrosymmetric nonmagnetic materials, and leads to a novel nonlinear spin-orbit torque in magnets. We reveal a quantum origin of this effect from the momentum space dipole of the anomalous spin polarizability. First-principles calculations predict sizable spin generations in several nonmagnetic hcp metals, in monolayer TiTe$_{2}$, and in ferromagnetic monolayer MnSe$_{2}$, which can be detected in experiment. Our work opens up the broad vista of nonlinear spintronics in both nonmagnetic and magnetic systems.Comment: 4 pages, 2 figure