임베디드 시스템 및 컴퓨터 구조 연구실

Originally developed specifically to accelerate graphics processing, GPUs are inherently designed to execute a massive volume of independent instructions simultaneously. As multi-core processors became the industry standard, the computing paradigm shifted heavily toward parallel programming to maximize efficiency. Recognizing this architectural advantage, modern GPUs have been extended to support general-purpose computational workloads. By making these massively multi-core processors fully programmable, the era of General-Purpose Computing on Graphics Processing Units (GPGPU) was established. Our lab models and optimizes GPGPU architectures to maximize both performance and energy efficiency. We focus deeply on the underlying hardware pipelines and the complete memory hierarchy—spanning local, global, constant, texture, and shared memory, alongside registers and interconnection buses. To rigorously evaluate our proposed architectural innovations, we utilize detailed C/C++ simulators to virtually execute and analyze complex workloads written in CUDA. Artificial Neural Networks (NNs) are powerful computing systems inspired by the biological structures of the human brain. They operate through interconnected nodes—or neurons—that process and transmit signals via weighted connections, which adjust dynamically during the learning phase. These neurons are aggregated into distinct layers, such as convolutional or fully-connected layers, enabling the network to model highly complex, nonlinear processes. Consequently, NNs have become the foundational technology driving modern breakthroughs in image recognition, speech processing, and natural language processing. Historically, these highly compute- and memory-intensive algorithms have been executed on GPGPUs. However, because GPUs