Myung Kuk Yoon

Latest

• Complex Tensor Core: Software-Hardware Co-Design for Accelerating Complex-Valued Neural Networks on GPUs
• Make Every Batch Count: Fault Entry Merging for Efficient Batching in Unified Virtual Memory
• BiKD: Bidirectional Kernel Decomposition for Large-Scale GCNs on GPU
• FINEA: An Efficient Neural Network Accelerator Exploiting Factorized Input Features
• TM-Training: An Energy-Efficient Tiered Memory System for Deep Learning Training in NPUs
• HALO: Hybrid Systolic Arrays via Logical Partitioning for Acceleration of Complex-Valued Neural Networks
• MOST: Memory Oversubscription-Aware Scheduling for Tensor Migration on GPU Unified Storage
• SSFFT: Energy-Efficient Selective Scaling for Fast Fourier Transform in Embedded GPUs
• Hierarchical Traversal Stack Design Using Shared Memory for GPU Ray Tracing
• HyMM: A Hybrid Sparse-Dense Matrix Multiplication Accelerator for GCNs
• Beyond VABlock: Improving Transformer Workloads through Aggressive Prefetching
• TLP Balancer: Predictive Thread Allocation for Multitenant Inference in Embedded GPUs
• VitBit: Enhancing Embedded GPU Performance for AI Workloads through Register Operand Packing
• SAVector: Vectored Systolic Arrays
• Conflict-Aware Compiler for Hierarchical Register File on GPUs
• Adaptive Kernel Merge and Fusion for Multi-Tenant Inference in Embedded GPUs
• Warped-MC: An Efficient Memory Controller Scheme for Massively Parallel Processors
• Analyzing GCN Aggregation on GPU
• GhostLeg: Selective Memory Coalescing for Secure GPU Architecture
• Analyzing Characteristics of Memory Timing Side-Channels in GPU
• Warped-Preexecution: A GPU Pre-Execution Approach for Improving Latency Hiding