4 citations · 6 across the 11 of their papers we have counts for
13 papers
LATCH: LLM-Assisted Timing Closure Harness for Post-Route FPGA Optimisation
Shu Gu, Matthew Youngman, Yuqin Zhao +5
Post-route FPGA timing closure remains expert-dependent and computationally expensive. We present LATCH (LLM-Assisted Timing Closure Harness), a two-layer framework that separates…
AgenticSizing: A Large Language Model-based Multi-Agent Framework for Analog Circuit Sizing
Yijia Hao, Pratibha Verma, Dongxu Guo +4
Analog circuit sizing remains a challenging and time-consuming task due to the large design space, strong performance trade-offs, and increasing circuit complexity in scaled techno…
Gen-TAS: A Generative AI-Aided Hardware-Software Task Allocation Framework for FPGA-GPP Heterogeneous Systems
Mary Kong, Yuqin Zhao, Semih Vazgecen +2
FPGA-GPP heterogeneous systems combine software flexibility with the performance and energy efficiency of reconfigurable hardware. However, determining which application tasks shou…
Vision-centric generative AI models: A software-hardware perspective
Eleni Tselepi, Cristian Sestito, Shady Agwa +1
Vision generative artificial intelligence (AI) has emerged as one of the most rapidly advancing areas of deep learning. The explosion of multimodal models has made them widely asso…
LLMs in Digital EDA: A perspective on shifting roles from Generation to Orchestration
Matthew Youngman, Cristian Sestito, Themis Prodromakis
Electronic design automation (EDA) has advanced engineering productivity through successive generations of tooling that progressively automate synthesis, optimisation, and verifica…
Non-frontal face recognition using GANs and memristor-based classifiers
Semih Vazgecen, Cristian Sestito, Spyros Stathopoulos +1
Face recognition systems have advanced significantly through deep learning techniques, delivering high performance and robustness in complex scenarios. However, these approaches in…
D-Legion: A Scalable Many-Core Architecture for Accelerating Matrix Multiplication in Quantized LLMs
Ahmed J. Abdelmaksoud, Cristian Sestito, Shiwei Wang +1
The performance gains obtained by large language models (LLMs) are closely linked to their substantial computational and memory requirements. Quantized LLMs offer significant advan…
A flexible language model-assisted electronic design automation framework
Cristian Sestito, Panagiota Kontou, Pratibha Verma +5
Large language models (LLMs) are transforming electronic design automation (EDA) by enhancing design stages such as schematic design, simulation, netlist synthesis, and place-and-r…
ADiP: Adaptive-Precision Systolic Array for Matrix Multiplication Acceleration
Ahmed J. Abdelmaksoud, Cristian Sestito, Shiwei Wang +1
Transformers are at the core of modern AI nowadays. They rely heavily on matrix multiplication and require efficient acceleration due to their substantial memory and computational…
3D-TrIM: A Memory-Efficient Spatial Computing Architecture for Convolution Workloads
Cristian Sestito, Ahmed J. Abdelmaksoud, Shady Agwa +1
The Von Neumann bottleneck, which relates to the energy cost of moving data from memory to on-chip core and vice versa, is a serious challenge in state-of-the-art AI architectures,…
L-Sort: On-chip Spike Sorting with Efficient Median-of-Median Detection and Localization-based Clustering
Yuntao Han, Yihan Pan, Xiongfei Jiang +4
Spike sorting is a critical process for decoding large-scale neural activity from extracellular recordings. The advancement of neural probes facilitates the recording of a high num…
TrIM, Triangular Input Movement Systolic Array for Convolutional Neural Networks: Architecture and Hardware Implementation
Cristian Sestito, Shady Agwa, Themis Prodromakis
Modern hardware architectures for Convolutional Neural Networks (CNNs), other than targeting high performance, aim at dissipating limited energy. Reducing the data movement cost be…
TrIM, Triangular Input Movement Systolic Array for Convolutional Neural Networks: Dataflow and Analytical Modelling
Cristian Sestito, Shady Agwa, Themis Prodromakis
In order to follow the ever-growing computational complexity and data intensity of state-of-the-art AI models, new computing paradigms are being proposed. These paradigms aim at ac…