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Young-Ho Gong

Assistant/Associate Professor

Soongsil University

South Korea

Research Interests

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About

Young-Ho Gong is an Assistant Professor at Soongsil University, South Korea. His research focuses on energy-efficient computing technologies, particularly in the areas of neural networks, memory architecture, and hardware optimization. Recent publications include work on quantization techniques for neural networks and the development of innovative memory solutions to enhance computational efficiency.

Articles (13)

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2026

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Young-Ho Gong

Research Interests

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About

Articles (13)

LayUp: Layer-wise Parallelization for Energy-Efficient Edge LLM Training Exploiting Unified Memory Characteristics

ReAx: Resource-Efficient Asynchronous Execution for Accelerating LLM Fine-Tuning at the Edge

DAWN: Efficient Distribution of Attention Workload in PIM-Enabled Systems for LLM Inference

Network Splitting Techniques and Their Optimization for Lightweight Ternary Neural Networks

SQUAD: A Scalable Quantization Accelerator Toward Energy-Efficient On-Device Quantization-Aware Training

ZEC ECC: A Zero-Byte Eliminating Compression-Based ECC Scheme for DRAM Reliability

HedgeRank: Heterogeneity-Aware, Energy-Efficient Partitioning of Personalized PageRank at the Edge

Scale-CIM: Precision-scalable computing-in-memory for energy-efficient quantized neural networks

Quant-PIM: An Energy-Efficient Processing-in-Memory Accelerator for Layerwise Quantized Neural Networks

Quantifying the Impact of Monolithic 3D (M3D) Integration on L1 Caches

A System-Level Exploration of Binary Neural Network Accelerators with Monolithic 3D Based Compute-in-Memory SRAM

Characterizing the Thermal Feasibility of Monolithic 3D Microprocessors

Monolithic 3D-Based SRAM/MRAM Hybrid Memory for an Energy-Efficient Unified L2 TLB-Cache Architecture

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