In this article, we present a spiking neural network (SNN) based on both SRAM processing-
in-memory (PIM) macro and on-chip unsupervised learning with Spike-Time-Dependent …

Future universal quantum computers solving problems of practical relevance are expected
to require at least 10 6 qubits, which is a massive scale-up from the present numbers of less …

This paper proposes a hybrid approach combining Recurrent Neural Network (RNN) and
polynomial regression methods for time-domain modeling of nonlinear circuits. The …

This paper presents a circuit design and an implementation of a four-channel 12-bit digital-to-
analog converter (DAC) with high-voltage operation and radiation-tolerant attribute using a …

An all-digital DLL is designed to generate low jittery 40 phases in a continuous lock range of
110 MHz to 1.4 GHz. The DLL is driven by dual loops-one for phase lock and the other for …

Driven by the ongoing challenge of designing high-accuracy digital-to-analog converters
(DACs) at the cost of a relatively small area occupation, optimal combination algorithms …

As Spiking Neural Networks (SNNs) gain popularity, more SNN components are developed.
SNN development requires a dedicated platform for testing and evaluating the various …

We present an integrated system-on-module for design-space exploration of neurosynaptic
behavior of complex, non-volatile memory enhanced, spiking neural networks. The system …

Nowadays, digital system technology is growing continuously, so the need of converting real-
time (analog) data to digital data plays an important role. Whereas with the advancement in …

This article introduces a novel macromodeling method based on a recurrent neural network
(RNN) called deep independently RNN (DIRNN). The proposed method applies to time …