Power and area efficiency of time-encoded neural networks make them highly suitable for smart portable applications. The main limitation here is the high sensitivity against circuit impairments like mismatch effects or PVT variations, which are usually mitigated by means of calibration circuitry. The purpose of this manuscript is to propose a novel time-encoded unit for Artificial Neural Networks, and make an in-depth analysis on how these effects degrade the accuracy in classification tasks. As an application example, image recognition tasks are selected using MNIST dataset. It is checked that the proposed unit does not require calibration to compensate mismatch effects, can be implemented with small devices, and shows accuracy degradation due to PVT variations that can be overcome with simple calibration or by increasing the number of bits used for the inputs and outputs of the unit. With a very simple neural network structure, 94.1 %-accuracy is achieved with a worst case simulated TOPS/W value of 24, and an area of 0.039 mm2in a 65 nm CMOS design node, with a data representation of 5 bits in the inputs and outputs of the unit and 4 bits used for the weights.

Analog-to-digital converters based on voltage-controlled oscillators (VCO-ADCs) can be employed in the readout circuit of a Microelectromechanical system (MEMS) microphone, as an alternative to classical voltage-encoding architectures. In the presence of a high sound pressure, larger than the microphone Acoustic Overload Point (AOP), or after a mechanical shock is applied to the sensor, undesired audible artifacts generated by spurious tones falling into the audio band can appear, impairing the shock recovery capability of the microphone. This paper presents a spurious-free VCO-ADC suitable for audio applications. The proposed idle-tone mitigation mechanism is based on introducing a sampling inaccuracy in the form of clock jitter modulation. Measurements of an ASIC implemented in 130 nm CMOS technology show the effectiveness of the proposed solution.

In this manuscript, we propose a time-domain, mostly digital band-pass filter architecture for audio signal feature extraction. The filter is composed of a single voltagecontrolled-oscillator (VCO) as signal encoder and monostables generating digital pulses at the edges of the pulse frequency modulated VCO output. The band-pass filtering phenomena are observed when we combine these digital pulses with different lengths, and its functionality is proved by the behavioral simulations performed with a circuit proposal built in MATLAB/Simulink. Compared to other state-of-the-art solutions, our filter architecture can take the benefit from advancement of digital technology nodes and design tools thanks to its mostly digital implementation. Moreover, the band-pass filter possesses friendly programmability, whose center frequency can be easily tuned by modifying the digital pulse length, and the single VCO encoder promises an easy calibration circuit. Finally, a recurrent neural network is trained and tested based on the features extracted from this architecture, the excellent speech/non-speech hitting rate shows the feasibility of our band-pass filters for voice activity tasks.

Studies on mastery learning applied to undergraduate engineering courses have revealed various positive results for students, including enhancing content mastery. Yet, there is a lack of information on how a mastery learning class influences the mindsets related to students' learning motivation, specifically students' growth mindset. In this Work-in--Progress Research paper, we describe the shifts in the growth mindset of a first-generation Latino college student, Rafael, who experienced a mastery learning environment, for the first time, in a Statics engineering course. Specifically, the research questions we investigated were: how did a mastery learning environment affect Rafael's mindset,? and how is Rafael's mindset related to the growth mindset? We longitudinally interviewed Rafael at three-time points. We used thematic analysis to extract three salient themes related to how Rafael's growth mindset evolved while experiencing a mastery learning environment and how he further developed academically throughout his mastery learning participation. First, Rafael described himself as having a “determination mindset” in his courses that led him to keep trying until he got the correct answer to a problem. Second, interviews affirmed that the mastery learning environment cultivated Rafael's “determination” predisposition and that the mastery learning environment kept Rafael motivated and hopeful, even after difficult assessments, which bolstered his growth mindset. Finally, the availability of retake opportunities allowed Rafael to further develop academically. Findings from our analysis show how a mastery learning environment supports students' growth mindset, which can lead to academic growth beyond what a traditional engineering course may offer.

The main limitation of voltage-controlled oscillator based analog-to-digital converters (VCO-ADC) is the VCO non-linear voltage-to-frequency response, especially with ring-oscillators. This issue strongly restricts the final resolution we are able to get from the converter. In addition, flicker noise is a limitation for low-bandwidth applications. We propose a novel way of compensating both the VCO non-linearity and the phase noise effects. By means of a time varying switched-capacitor circuit, we build a purely analog compensation loop where jitter clock and DAC non-linearity do not suppose a limitation. The loop is closed with an opamp that provides gain to the compensation operation. The idea is proposed theoretically and tested by simulations, showing a good performance for audio or sensing applications.

Software failures in server applications are a significant problem for preserving system availability. We present ASSURE, a system that introduces rescue points that recover software from unknown faults while maintaining both system integrity and availability, by mimicking system behavior under known error conditions. Rescue points are locations in existing application code for handling a given set of programmer-anticipated failures, which are automatically repurposed and tested for safely enabling fault recovery from a larger class of (unanticipated) faults. When a fault occurs at an arbitrary location in the program, ASSURE restores execution to an appropriate rescue point and induces the program to recover execution by virtualizing the program's existing error-handling facilities. Rescue points are identified using fuzzing, implemented using a fast coordinated checkpoint-restart mechanism that handles multi-process and multi-threaded applications, and, after testing, are injected into production code using binary patching. We have implemented an ASSURE Linux prototype that operates without application source code and without base operating system kernel changes. Our experimental results on a set of real-world server applications and bugs show that ASSURE enabled recovery for all of the bugs tested with fast recovery times, has modest performance overhead, and provides automatic self-healing orders of magnitude faster than current human-driven patch deployment methods.

In this paper we introduce a topology control algorithm for increasing transport robustness and efficiency by creating two simultaneous communication paths between the source and destination nodes of a data flow. We make use of a cross-layer substrate that allows us to detect the flows of data in the network while also providing finer control of the routing. Because information of flow traffic in the network is available, the algorithm attempts to achieve intra-flow interference reduction by exploiting node mobility.

Crowd simulation can be considered as a special case of virtual environments where avatars are intelligent agents instead of user-driven entities. These applications require both rendering visually plausible images of the virtual world and managing the behavior of autonomous agents. Although several proposals have focused on the software architectures for these systems, the scalability of crowd simulation is still an open issue. In this paper, we propose a scalable architecture that can manage large crowds of autonomous agents at interactive rates. This proposal consists of enhancing a previously proposed architecture through the efficient parallelization of the action server and the distribution of the semantic database. In this way, the system bottleneck is removed, and new action servers (hosted each one on a new computer) can be added as necessary. The evaluation results show that the proposed architecture is able to fully exploit the underlying hardware platform, regardless of both the number and the kind of computers that form the system. Therefore, this system architecture provides the scalability required for large-scale crowd simulation.