1 citations · 1 across the 7 of their papers we have counts for
8 papers
Beyond HBM-on-GPU: Thermal Design Envelope for 3D Volumetric DRAM-on-GPU Integration
Yukai Chen, Melina Lofrano, Khakim Akhunov +12
The scaling of GPUs for AI and HPC workloads is increasingly constrained by the capacity, bandwidth, and thermal limits of both 2.5D HBM-GPU and direct-stacked 3D HBM-on-GPU integr…
DTCO of NOR-Type IGZO FeFETs for 3D Heterogeneous AI Memories: A Read-Centric Perspective
Yang Xiang, Zhuo Chen, Nicolò Ronchi +8
InGaZnO (IGZO)-channel FeFETs have attracted notable interest thanks to recent advances in endurance, opening up their application space for read-dominated AI memory tiers. This wo…
Technology solutions targeting the performance of gen-AI inference in resource constrained platforms
Joyjit Kundu, Joshua Klein, Aakash Patel +1
The rise of generative AI workloads, particularly language model inference, is intensifying on/off-chip memory pressure. Multimodal inputs such as video streams or images and downs…
LIMO: Low-Power In-Memory-Annealer and Matrix-Multiplication Primitive for Edge Computing
Amod Holla, Sumedh Chatterjee, Sutanu Sen +5
Combinatorial optimization (CO) underpins applications in science and engineering, ranging from logistics to electronic design automation. A classic example is the NP-complete Trav…
Physical Design Exploration of a Wire-Friendly Domain-Specific Processor for Angstrom-Era Nodes
Lorenzo Ruotolo, Lara Orlandic, Pengbo Yu +8
This paper presents the physical design exploration of a domain-specific processor (DSIP) architecture targeted at machine learning (ML), addressing the challenges of interconnect…
Thermal Implications of Non-Uniform Power in BSPDN-Enabled 2.5D/3D Chiplet-based Systems-in-Package using Nanosheet Technology
Yukai Chen, Massimiliano Di Todaro, Bjorn Vermeersch +5
Advances in nanosheet technologies have significantly increased power densities, exacerbating thermal management challenges in 2.5D/3D chiplet-based Systems-in-Package (SiP). While…
A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO
Jonas Svedas, Hannah Watson, Nathan Laubeuf +6
Distributed deep neural networks (DNNs) have become a cornerstone for scaling machine learning to meet the demands of increasingly complex applications. However, the rapid growth i…
TAXI: Traveling Salesman Problem Accelerator with X-bar-based Ising Macros Powered by SOT-MRAMs and Hierarchical Clustering
Sangmin Yoo, Amod Holla, Sourav Sanyal +5
Ising solvers with hierarchical clustering have shown promise for large-scale Traveling Salesman Problems (TSPs), in terms of latency and energy. However, most of these methods sti…