Zobrazeno 1 - 10
of 110
pro vyhledávání: '"Zhu, Hanqing"'
Analog front-end design heavily relies on specialized human expertise and costly trial-and-error simulations, which motivated many prior works on analog design automation. However, efficient and effective exploration of the vast and complex design sp
Externí odkaz:
http://arxiv.org/abs/2407.07346
Analog layout synthesis faces significant challenges due to its dependence on manual processes, considerable time requirements, and performance instability. Current Bayesian Optimization (BO)-based techniques for analog layout synthesis, despite thei
Externí odkaz:
http://arxiv.org/abs/2406.05250
Autor:
Ning, Shupeng, Zhu, Hanqing, Feng, Chenghao, Gu, Jiaqi, Jiang, Zhixing, Ying, Zhoufeng, Midkiff, Jason, Jain, Sourabh, Hlaing, May H., Pan, David Z., Chen, Ray T.
In recent decades, the demand for computational power has surged, particularly with the rapid expansion of artificial intelligence (AI). As we navigate the post-Moore's law era, the limitations of traditional electrical digital computing, including p
Externí odkaz:
http://arxiv.org/abs/2403.14806
FPGA macro placement plays a pivotal role in routability and timing closer to the modern FPGA physical design flow. In modern FPGAs, macros could be subject to complex cascade shape constraints requiring instances to be placed in consecutive sites. I
Externí odkaz:
http://arxiv.org/abs/2311.08582
Autor:
Feng, Chenghao, Gu, Jiaqi, Zhu, Hanqing, Tang, Rongxing, Ning, Shupeng, Hlaing, May, Midkiff, Jason, Jain, Sourabh, Pan, David Z., Chen, Ray T.
The optical neural network (ONN) is a promising hardware platform for next-generation neuromorphic computing due to its high parallelism, low latency, and low energy consumption. However, previous integrated photonic tensor cores (PTCs) consume numer
Externí odkaz:
http://arxiv.org/abs/2305.19592
Autor:
Zhu, Hanqing, Gu, Jiaqi, Wang, Hanrui, Jiang, Zixuan, Zhang, Zhekai, Tang, Rongxing, Feng, Chenghao, Han, Song, Chen, Ray T., Pan, David Z.
The wide adoption and significant computing resource of attention-based transformers, e.g., Vision Transformers and large language models (LLM), have driven the demand for efficient hardware accelerators. There is a growing interest in exploring phot
Externí odkaz:
http://arxiv.org/abs/2305.19533
Photonic computing shows promise for transformative advancements in machine learning (ML) acceleration, offering ultra-fast speed, massive parallelism, and high energy efficiency. However, current photonic tensor core (PTC) designs based on standard
Externí odkaz:
http://arxiv.org/abs/2305.19505
Transformers have achieved great success in machine learning applications. Normalization techniques, such as Layer Normalization (LayerNorm, LN) and Root Mean Square Normalization (RMSNorm), play a critical role in accelerating and stabilizing the tr
Externí odkaz:
http://arxiv.org/abs/2305.14858
Autor:
Jin, Harrison, Zhu, Hanqing, Zhu, Keren, Leonard, Thomas, Kwon, Jaesuk, Alamdar, Mahshid, Kim, Kwangseok, Park, Jungsik, Hase, Naoki, Pan, David Z., Incorvia, Jean Anne C.
With the rise in in-memory computing architectures to reduce the compute-memory bottleneck, a new bottleneck is present between analog and digital conversion. Analog content-addressable memories (ACAM) are being recently studied for in-memory computi
Externí odkaz:
http://arxiv.org/abs/2301.04598
Autor:
Gu, Jiaqi, Gao, Zhengqi, Feng, Chenghao, Zhu, Hanqing, Chen, Ray T., Boning, Duane S., Pan, David Z.
Optical computing is an emerging technology for next-generation efficient artificial intelligence (AI) due to its ultra-high speed and efficiency. Electromagnetic field simulation is critical to the design, optimization, and validation of photonic de
Externí odkaz:
http://arxiv.org/abs/2209.10098