arXiv subjects

Sung-Jin Kim

Publications and source records attributed to Sung-Jin Kim.

21 records · Page 2Linked to original sources

arXiv2020

Open-Source Synthesizable Analog Blocks for High-Speed Link Designs: 20-GS/s 5b ENOB Analog-to-Digital Converter and 5-GHz Phase Interpolator

Using digital standard cells and digital place-and-route (PnR) tools, we created a 20 GS/s, 8-bit analog-to-digital converter (ADC) for use in high-speed serial link applications with an ENOB of 5.6, a DNL of 0.96 LSB, and an INL of 2.39 LSB, which dissipated 175 mW in 0.102 mm2 in a 16nm technology. The design is entirely described by HDL so that it can be ported to other processes with minimal effort and shared as open source.

cs.AR↗

arXiv2016

MultiDK: A Multiple Descriptor Multiple Kernel Approach for Molecular Discovery and Its Application to The Discovery of Organic Flow Battery Electrolytes

We propose a multiple descriptor multiple kernel (MultiDK) method for efficient molecular discovery using machine learning. We show that the MultiDK method improves both the speed and the accuracy of molecular property prediction. We apply the method to the discovery of electrolyte molecules for aqueous redox flow batteries. Using \emph{multiple-type - as opposed to single-type - descriptors}, more relevant features for machine learning can be obtained. Following the principle of the 'wisdom of the crowds', the combination of multiple-type descriptors significantly boosts prediction performance. Moreover, MultiDK can exploit irregularities between molecular structure and property relations better than the linear regression method by employing multiple kernels - more than one kernel functions for a set of the input descriptors. The multiple kernels consist of the Tanimoto similarity function and a linear kernel for a set of binary descriptors and a set of non-binary descriptors, respectively. Using MultiDK, we achieve average performance of $r^2 = 0.92$ with a set of molecules for solubility prediction. We also extend MultiDK to predict pH-dependent solubility and apply it to solubility estimation of quinone molecules with ionizable functional groups as strong candidates of flow battery electrolytes.

physics.chem-ph↗

arXiv2011

Memristor-based Synaptic Networks and Logical Operations Using In-Situ Computing

We present new computational building blocks based on memristive devices. These blocks, can be used to implement either supervised or unsupervised learning modules. This is achieved using a crosspoint architecture which is an efficient array implementation for nanoscale two-terminal memristive devices. Based on these blocks and an experimentally verified SPICE macromodel for the memristor, we demonstrate that firstly, the Spike-Timing-Dependent Plasticity (STDP) can be implemented by a single memristor device and secondly, a memristor-based competitive Hebbian learning through STDP using a $1\times 1000$ synaptic network. This is achieved by adjusting the memristor's conductance values (weights) as a function of the timing difference between presynaptic and postsynaptic spikes. These implementations have a number of shortcomings due to the memristor's characteristics such as memory decay, highly nonlinear switching behaviour as a function of applied voltage/current, and functional uniformity. These shortcomings can be addressed by utilising a mixed gates that can be used in conjunction with the analogue behaviour for biomimetic computation. The digital implementations in this paper use in-situ computational capability of the memristor.

cond-mat.mtrl-sci↗