462 citations · 1.2k across the 16 of their papers we have counts for
16 papers
Duplex: A Device for Large Language Models with Mixture of Experts, Grouped Query Attention, and Continuous Batching
Sungmin Yun, Kwanhee Kyung, Juhwan Cho +6
Large language models (LLMs) have emerged due to their capability to generate high-quality content across diverse contexts. To reduce their explosively increasing demands for compu…
DRAMScope: Uncovering DRAM Microarchitecture and Characteristics by Issuing Memory Commands
Hwayong Nam, Seungmin Baek, Minbok Wi +5
The demand for precise information on DRAM microarchitectures and error characteristics has surged, driven by the need to explore processing in memory, enhance reliability, and mit…
CiFHER: A Chiplet-Based FHE Accelerator with a Resizable Structure
Sangpyo Kim, Jongmin Kim, Jaeyoung Choi +1
Fully homomorphic encryption (FHE) is in the spotlight as a definitive solution for privacy, but the high computational overhead of FHE poses a challenge to its practical adoption.…
Corona: System Implications of Emerging Nanophotonic Technology
Dana Vantrease, Robert Schreiber, Matteo Monchiero +7
We expect that many-core microprocessors will push performance per chip from the 10 gigaflop to the 10 teraflop range in the coming decade. To support this increased performance, m…
RETROSPECTIVE: Corona: System Implications of Emerging Nanophotonic Technology
Dana Vantrease, Robert Schreiber, Matteo Monchiero +7
The 2008 Corona effort was inspired by a pressing need for more of everything, as demanded by the salient problems of the day. Dennard scaling was no longer in effect. A lot of com…
X-ray: Discovering DRAM Internal Structure and Error Characteristics by Issuing Memory Commands
Hwayong Nam, Seungmin Baek, Minbok Wi +5
The demand for accurate information about the internal structure and characteristics of dynamic random-access memory (DRAM) has been on the rise. Recent studies have explored the s…
Demystifying CXL Memory with Genuine CXL-Ready Systems and Devices
Yan Sun, Yifan Yuan, Zeduo Yu +11
The ever-growing demands for memory with larger capacity and higher bandwidth have driven recent innovations on memory expansion and disaggregation technologies based on Compute eX…
HyPHEN: A Hybrid Packing Method and Optimizations for Homomorphic Encryption-Based Neural Networks
Donghwan Kim, Jaiyoung Park, Jongmin Kim +2
Convolutional neural network (CNN) inference using fully homomorphic encryption (FHE) is a promising private inference (PI) solution due to the capability of FHE that enables offlo…
ARK: Fully Homomorphic Encryption Accelerator with Runtime Data Generation and Inter-Operation Key Reuse
Jongmin Kim, Gwangho Lee, Sangpyo Kim +4
Homomorphic Encryption (HE) is one of the most promising post-quantum cryptographic schemes that enable privacy-preserving computation on servers. However, noise accumulates as we…
AESPA: Accuracy Preserving Low-degree Polynomial Activation for Fast Private Inference
Jaiyoung Park, Michael Jaemin Kim, Wonkyung Jung +1
Hybrid private inference (PI) protocol, which synergistically utilizes both multi-party computation (MPC) and homomorphic encryption, is one of the most prominent techniques for PI…
BTS: An Accelerator for Bootstrappable Fully Homomorphic Encryption
Sangpyo Kim, Jongmin Kim, Michael Jaemin Kim +4
Homomorphic encryption (HE) enables the secure offloading of computations to the cloud by providing computation on encrypted data (ciphertexts). HE is based on noisy encryption sch…
Mithril: Cooperative Row Hammer Protection on Commodity DRAM Leveraging Managed Refresh
Michael Jaemin Kim, Jaehyun Park, Yeonhong Park +5
Since its public introduction in the mid-2010s, the Row Hammer (RH) phenomenon has drawn significant attention from the research community due to its security implications. Althoug…
Accelerating Number Theoretic Transformations for Bootstrappable Homomorphic Encryption on GPUs
Sangpyo Kim, Wonkyung Jung, Jaiyoung Park +1
Homomorphic encryption (HE) draws huge attention as it provides a way of privacy-preserving computations on encrypted messages. Number Theoretic Transform (NTT), a specialized form…
HEAAN Demystified: Accelerating Fully Homomorphic Encryption Through Architecture-centric Analysis and Optimization
Wonkyung Jung, Eojin Lee, Sangpyo Kim +5
Homomorphic Encryption (HE) draws a significant attention as a privacy-preserving way for cloud computing because it allows computation on encrypted messages called ciphertexts. Am…
Restructuring Batch Normalization to Accelerate CNN Training
Wonkyung Jung, Daejin Jung, and Byeongho Kim +3
Batch Normalization (BN) has become a core design block of modern Convolutional Neural Networks (CNNs). A typical modern CNN has a large number of BN layers in its lean and deep ar…
Partitioning Compute Units in CNN Acceleration for Statistical Memory Traffic Shaping
Daejin Jung, Sunjung Lee, Wonjong Rhee +1
The design complexity of CNNs has been steadily increasing to improve accuracy. To cope with the massive amount of computation needed for such complex CNNs, the latest solutions ut…