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Found 10 papers, 2 papers with code
EasyACIM: An End-to-End Automated Analog CIM with Synthesizable Architecture and Agile Design Space Exploration
Leveraging the multi-objective genetic algorithm (MOGA)-based design space explorer, EasyACIM can obtain high-quality ACIM solutions based on the proposed synthesizable architecture, targeting versatile application scenarios.
PDNNet: PDN-Aware GNN-CNN Heterogeneous Network for Dynamic IR Drop Prediction
We are the first work to apply graph structure to deep-learning based dynamic IR drop prediction method.
Imbalanced Large Graph Learning Framework for FPGA Logic Elements Packing Prediction
With imbalanced distribution of packed and unpacked logic elements, we further propose techniques such as graph oversampling and mini-batch training for this imbalanced learning task in large circuit graphs.
HybridNet: Dual-Branch Fusion of Geometrical and Topological Views for VLSI Congestion Prediction
Accurate early congestion prediction can prevent unpleasant surprises at the routing stage, playing a crucial character in assisting designers to iterate faster in VLSI design cycles.
CircuitNet: An Open-Source Dataset for Machine Learning Applications in Electronic Design Automation (EDA)
The electronic design automation (EDA) community has been actively exploring machine learning (ML) for very large-scale integrated computer-aided design (VLSI CAD).
LHNN: Lattice Hypergraph Neural Network for VLSI Congestion Prediction
Precise congestion prediction from a placement solution plays a crucial role in circuit placement.
Towards Machine Learning for Placement and Routing in Chip Design: a Methodological Overview
Placement and routing are two indispensable and challenging (NP-hard) tasks in modern chip design flows.
Taskflow: A Lightweight Parallel and Heterogeneous Task Graph Computing System
Taskflow introduces an expressive task graph programming model to assist developers in the implementation of parallel and heterogeneous decomposition strategies on a heterogeneous computing platform.
Towards a Theoretical Understanding of Hashing-Based Neural Nets
In this paper, we provide provable guarantees on some hashing-based parameter reduction methods in neural nets.
Learning Long Term Dependencies via Fourier Recurrent Units
In this paper we propose a simple recurrent architecture, the Fourier Recurrent Unit (FRU), that stabilizes the gradients that arise in its training while giving us stronger expressive power.
