12 papers
Decoupling Trap States in Amorphous Oxide Semiconductor Transistor Using a Conductive-Semiconducting-Conductive MOS Structure
Kai Chen, Wei Zhang, Xuanyao Fong +2
We propose a novel amorphous oxide semiconductor (AOS)-based conductive-semiconducting-conductive (CSC) MOS test structure to effectively sense, discriminate, and analyze distinct…
LLM Inference on IMC-NoC Architecture with Balanced Dataflow and Fine-Grained Parallelism
Yimin Wang, Yue Jiet Chong, Xuanyao Fong
LLM inference has become an essential service, yet it imposes unprecedented demands on memory bandwidth, computational density, and communication efficiency. While IMC is a promisi…
CHIPSMORE: Compute-in-Interconnect and -Memory Chiplets for Multi-Mode Multi-Request LLM Inference Acceleration
Yue Jiet Chong, Yimin Wang, Zhen Wu +3
Large language model (LLM) inference exhibits substantial variability across adaptation modes, context lengths, and request concurrency, creating challenges for maintaining high ut…
CIMERA: Compute-in-Interconnect and Memory with Reconfigurable Precision for LLM Inference
Yue Jiet Chong, Yimin Wang, Wei Zhang +1
LLM impose significant computational and memory demands, creating challenges for energy-efficient inference across platforms ranging from data centers to power-constrained edge dev…
Continuous Optimization for Satisfiability Modulo Theories on Linear Real Arithmetic
Yunuo Cen, Daniel Ebler, Xuanyao Fong
Efficient solutions for satisfiability modulo theories (SMT) are integral in industrial applications such as hardware verification and design automation. Existing approaches are pr…
DART-ing Through the Drift: Dynamic Tracing of Knowledge Neurons for Adaptive Inference-Time Pruning
Abhishek Tyagi, Yunuo Cen, Shrey Dhorajiya +2
Large Language Models (LLMs) exhibit substantial parameter redundancy, particularly in Feed-Forward Networks (FFNs). Existing pruning methods suffer from two primary limitations. F…
PRIMAL: Processing-In-Memory Based Low-Rank Adaptation for LLM Inference Accelerator
Yue Jiet Chong, Yimin Wang, Zhen Wu +1
This paper presents PRIMAL, a processing-in-memory (PIM) based large language model (LLM) inference accelerator with low-rank adaptation (LoRA). PRIMAL integrates heterogeneous PIM…
PICNIC: Silicon Photonic Interconnected Chiplets with Computational Network and In-memory Computing for LLM Inference Acceleration
Yue Jiet Chong, Yimin Wang, Zhen Wu +1
This paper presents a 3D-stacked chiplets based large language model (LLM) inference accelerator, consisting of non-volatile in-memory-computing processing elements (PEs) and Inter…
FourierCSP: Differentiable Constraint Satisfaction Problem Solving by Walsh-Fourier Expansion
Yunuo Cen, Zixuan Wang, Jintao Zhang +2
The Constraint-satisfaction problem (CSP) is fundamental in mathematics, physics, and theoretical computer science. Continuous local search (CLS) solvers, as recent advancements, c…
LEAP: LLM Inference on Scalable PIM-NoC Architecture with Balanced Dataflow and Fine-Grained Parallelism
Yimin Wang, Yue Jiet Chong, Xuanyao Fong
Large language model (LLM) inference has been a prevalent demand in daily life and industries. The large tensor sizes and computing complexities in LLMs have brought challenges to…
Hierarchical Recording Architecture for Three-Dimensional Magnetic Recording
Yugen Jian, Ke Luo, Jincai Chen +1
Three-dimensional magnetic recording (3DMR) is a highly promising approach to achieving ultra-large data storage capacity in hard disk drives. One of the greatest challenges for 3D…
Analysis of Higher-Order Ising Hamiltonians
Yunuo Cen, Zhiwei Zhang, Zixuan Wang +2
It is challenging to scale Ising machines for industrial-level problems due to algorithm or hardware limitations. Although higher-order Ising models provide a more compact encoding…