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09/14/2023
VerilogEval: Evaluating Large Language Models for Verilog Code Generation
The increasing popularity of large language models (LLMs) has paved the ...
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11/30/2022
HEAT: Hardware-Efficient Automatic Tensor Decomposition for Transformer Compression
Transformers have attained superior performance in natural language proc...
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10/27/2022
An Adversarial Active Sampling-based Data Augmentation Framework for Manufacturable Chip Design
Lithography modeling is a crucial problem in chip design to ensure a chi...
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07/08/2022
Large Scale Mask Optimization Via Convolutional Fourier Neural Operator and Litho-Guided Self Training
Machine learning techniques have been extensively studied for mask optim...
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06/13/2022
Optimal Clipping and Magnitude-aware Differentiation for Improved Quantization-aware Training
Data clipping is crucial in reducing noise in quantization operations an...
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03/12/2022
Generic Lithography Modeling with Dual-band Optics-Inspired Neural Networks
Lithography simulation is a critical step in VLSI design and optimizatio...
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03/11/2022
GATSPI: GPU Accelerated Gate-Level Simulation for Power Improvement
In this paper, we present GATSPI, a novel GPU accelerated logic gate sim...
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06/26/2021
Low-Precision Training in Logarithmic Number System using Multiplicative Weight Update
Representing deep neural networks (DNNs) in low-precision is a promising...
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03/16/2021
Softermax: Hardware/Software Co-Design of an Efficient Softmax for Transformers
Transformers have transformed the field of natural language processing. ...
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02/12/2021
Verifying High-Level Latency-Insensitive Designs with Formal Model Checking
Latency-insensitive design mitigates increasing interconnect delay and e...
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02/08/2021
VS-Quant: Per-vector Scaled Quantization for Accurate Low-Precision Neural Network Inference
Quantization enables efficient acceleration of deep neural networks by r...
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12/19/2020
MAVIREC: ML-Aided Vectored IR-DropEstimation and Classification
Vectored IR drop analysis is a critical step in chip signoff that checks...
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11/26/2020
PowerNet: Transferable Dynamic IR Drop Estimation via Maximum Convolutional Neural Network
IR drop is a fundamental constraint required by almost all chip designs....
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11/26/2020
FIST: A Feature-Importance Sampling and Tree-Based Method for Automatic Design Flow Parameter Tuning
Design flow parameters are of utmost importance to chip design quality a...
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05/23/2017