Suchergebnisse - "2023 60th ACM/IEEE Design Automation Conference (DAC)"

Scalable Optimal Layout Synthesis for NISQ Quantum Processors von Lin, Wan-Hsuan, Kimko, Jason, Tan, Bochen, Bjorner, Nikolaj, Cong, Jason

“… Due to its effect on the success rate of a quantum circuit, quantum layout synthesis is a crucial step for circuit compilation. As such, having a layout …”
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Gamora: Graph Learning based Symbolic Reasoning for Large-Scale Boolean Networks von Wu, Nan, Li, Yingjie, Hao, Cong, Dai, Steve, Yu, Cunxi, Xie, Yuan

“… Reasoning high-level abstractions from bit-blasted Boolean networks (BNs) such as gate-level netlists can significantly benefit functional verification, logic …”
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Chiplets: How Small is too Small? von Graening, Alexander, Pal, Saptadeep, Gupta, Puneet

“… As chiplet systems increase in popularity, it is important to revisit the tradeoffs for converting a monolithic design to a chiplet system. Chip yield, …”
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Compiler Optimization for Quantum Computing Using Reinforcement Learning von Quetschlich, Nils, Burgholzer, Lukas, Wille, Robert

“… Any quantum computing application, once encoded as a quantum circuit, must be compiled before being executable on a quantum computer. Similar to classical …”
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HexaMesh: Scaling to Hundreds of Chiplets with an Optimized Chiplet Arrangement von Iff, Patrick, Besta, Maciej, Cavalcante, Matheus, Fischer, Tim, Benini, Luca, Hoefler, Torsten

“… 2.5D integration is an important technique to tackle the growing cost of manufacturing chips in advanced technology nodes. This poses the challenge of …”
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Restructure-Tolerant Timing Prediction via Multimodal Fusion von Wang, Ziyi, Liu, Siting, Pu, Yuan, Chen, Song, Ho, Tsung-Yi, Yu, Bei

“… Fast and accurate pre-routing timing prediction is crucial in the very-large-scale integration (VLSI) design flow. Existing machine learning (ML)-assisted …”
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Ising-CF: A Pathbreaking Collaborative Filtering Method Through Efficient Ising Machine Learning von Liu, Zhuo, Yang, Yunan, Pan, Zhenyu, Sharma, Anshujit, Hasan, Amit, Ding, Caiwen, Li, Ang, Huang, Michael, Geng, Tong

“… Due to the Ising model's strong expressivity and Ising machines' unique computational power, it is highly desired if Ising-based learning can be used in …”
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DeepOHeat: Operator Learning-based Ultra-fast Thermal Simulation in 3D-IC Design von Liu, Ziyue, Li, Yixing, Hu, Jing, Yu, Xinling, Shiau, Shinyu, Ai, Xin, Zeng, Zhiyu, Zhang, Zheng

“… Thermal issue is a major concern in 3D integrated circuit (IC) design. Thermal optimization of 3D IC often requires massive expensive PDE simulations. Neural …”
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Graph Representation Learning for Microarchitecture Design Space Exploration von Yi, Xiaoling, Lu, Jialin, Xiong, Xiankui, Xu, Dong, Shang, Li, Yang, Fan

“… Design optimization of modern microprocessors is a complex task due to the exponential growth of the design space. This work presents GRL-DSE, an automatic …”
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Primer: Fast Private Transformer Inference on Encrypted Data von Zheng, Mengxin, Lou, Qian, Jiang, Lei

“… It is increasingly important to enable privacy-preserving inference for cloud services based on Transformers. Post-quantum cryptographic techniques, e.g., …”
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Hybrid Gate-Pulse Model for Variational Quantum Algorithms von Liang, Zhiding, Song, Zhixin, Cheng, Jinglei, He, Zichang, Liu, Ji, Wang, Hanrui, Qin, Ruiyang, Wang, Yiru, Han, Song, Qian, Xuehai, Shi, Yiyu

“… Current quantum programs are mostly synthesized and compiled on the gate-level, where quantum circuits are composed of quantum gates. The gate-level workflow, …”
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Privacy-Preserving DNN Training with Prefetched Meta-Keys on Heterogeneous Neural Network Accelerators von Li, Qiushi, Ren, Ju, Zhang, Yan, Song, Chengru, Liao, Yiqiao, Zhang, Yaoxue

“… The embedded software may migrate the collected data to the server for DNN computation acceleration, which may compromise privacy. We propose a DNN computation …”
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Efficient ILT via Multi-level Lithography Simulation von Sun, Shuyuan, Yang, Fan, Yu, Bei, Shang, Li, Zeng, Xuan

“… Inverse Lithography Technology (ILT) is a widely investigated method to improve the yield of chip manufacturing. However, high computational complexity and …”
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RL-MUL: Multiplier Design Optimization with Deep Reinforcement Learning von Zuo, Dongsheng, Ouyang, Yikang, Ma, Yuzhe

“… Multiplication is a fundamental operation in many applications, and multipliers are widely adopted in various circuits. However, optimizing multipliers is …”
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COSA:Co-Operative Systolic Arrays for Multi-head Attention Mechanism in Neural Network using Hybrid Data Reuse and Fusion Methodologies von Wang, Zhican, Wang, Gang, Jiang, Honglan, Xu, Ningyi, He, Guanghui

“… Attention mechanism acceleration is becoming increasingly vital to achieve superior performance in deep learning tasks. Existing accelerators are commonly …”
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RoSE: Robust Analog Circuit Parameter Optimization with Sampling-Efficient Reinforcement Learning von Gao, Jian, Cao, Weidong, Zhang, Xuan

“… Design automation of analog circuits has been a long-standing challenge in the integrated circuit field. Recently, multiple methods based on learning or …”
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Automating Constraint-Aware Datapath Optimization using E-Graphs von Coward, Samuel, Constantinides, George A., Drane, Theo

“… Numerical hardware design requires aggressive optimization, where designers exploit branch constraints, creating optimization opportunities that are valid only …”
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OmniBoost: Boosting Throughput of Heterogeneous Embedded Devices under Multi-DNN Workload von Karatzas, Andreas, Anagnostopoulos, Iraklis

“… Modern Deep Neural Networks (DNNs) exhibit profound efficiency and accuracy properties. This has introduced application workloads that comprise of multiple DNN …”
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Input-Aware Dynamic Timestep Spiking Neural Networks for Efficient In-Memory Computing von Li, Yuhang, Moitra, Abhishek, Geller, Tamar, Panda, Priyadarshini

“… Spiking Neural Networks (SNNs) have recently attracted widespread research interest as an efficient alternative to traditional Artificial Neural Networks …”
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HAIMA: A Hybrid SRAM and DRAM Accelerator-in-Memory Architecture for Transformer von Ding, Yan, Liu, Chubo, Duan, Mingxing, Chang, Wanli, Li, Keqin, Li, Kenli

“… Through the attention mechanism, Transformer-based large-scale deep neural networks (LSDNNs) have demonstrated remarkable achievements in artificial …”
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