The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
In recent years, deep learning has become widespread for various real-world recognition tasks. In addition to recognition accuracy, energy efficiency is another grand challenge to enable local intelligence in edge devices. In this paper, we investigate the adoption of monolithic 3D IC (M3D) technology for deep learning hardware design, using speech recognition as a test vehicle. M3D has recently proven...
The effort to integrate emotions into human-computer interaction (HCI) system has attracted broad attentions. Automatic emotion recognition enables the HCI to become more intelligent and user friendly. Although numerous studies have been performed in this field, emotion recognition is still an extremely challenging task, especially in real-world practice usage. In this work, probabilistic neural network...
Memristor-based neuromorphic computing system provides a promising solution to significantly boost the power efficiency of computing system. Memristor-based neuromorphic computing system has a wide range of design choices, such as the various memristor crossbar cell designs and different parallelism degrees of peripheral circuits. However, a memristor-based neuromorphic computing system simulator,...
The phenomenon of metal-insulator-transition (MIT) in strongly correlated oxides, such as NbO2, have shown the oscillation behavior in recent experiments. In this work, the MIT based two-terminal device is proposed as a compact oscillation neuron for the parallel read operation from the resistive synaptic array. The weighted sum is represented by the frequency of the oscillation neuron. Compared to...
This paper proposes a parallel architecture with resistive crosspoint array. The design of its two essential operations, Read and Write, is inspired by the biophysical behavior of a neural system, such as integrate-and-fire and time-dependent synaptic plasticity. The proposed hardware consists of an array with resistive random access memory (RRAM) and CMOS peripheral circuits, which perform matrix...
A workload-aware low-power neuromorphic controller for dynamic power and thermal management in VLSI systems is presented. The neuromorphic controller predicts future workload and temperature values based on the past values and preemptively regulates supply voltage and frequency. Our specific contributions include: (1) implementation of a digital and analog version of the controller in 45nm CMOS technology,...
A workload-aware low-power neuromorphic controller for dynamic voltage and frequency scaling (DVFS) in very large scale integration (VLSI) systems is presented. The neuromorphic controller predicts future workload values and preemptively regulates supply voltage and frequency based on past workload profile. Our specific contributions include: 1) implementation of a digital and analog version of the...
A workload-aware low-power neuromorphic controller for dynamic voltage scaling in VLSI systems is presented. The neuromorphic controller predicts future workload values and preemptively regulates supply voltage based on past workload profile. Our specific contributions include: (1) implementation of a digital and analog version of the controller in 45nm CMOS technology, resulting in 3% performance...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.