Zobrazeno 1 - 10
of 457
pro vyhledávání: '"Zhou, Peipei"'
Autor:
Brazzle, Preston, Morris III, Benjamin F., McKinney, Evan, Zhou, Peipei, Hu, Jingtong, Khan, Asif Ali, Jones, Alex K.
Computing-in-memory (CIM) promises to alleviate the Von Neumann bottleneck and accelerate data-intensive applications. Depending on the underlying technology and configuration, CIM enables implementing compute primitives in place, such as multiplicat
Externí odkaz:
http://arxiv.org/abs/2407.21661
The rising demand for on-demand, high-performance computing has led to the growth of data centers, which in turn presents both challenges and opportunities for addressing their environmental impact. Traditionally, sustainability efforts in data cente
Externí odkaz:
http://arxiv.org/abs/2403.04976
Autor:
Zhuang, Jinming, Yang, Zhuoping, Ji, Shixin, Huang, Heng, Jones, Alex K., Hu, Jingtong, Shi, Yiyu, Zhou, Peipei
Publikováno v:
2024 ACM/SIGDA International Symposium on Field Programmable Gate Arrays (FPGA '24)
With the increase in the computation intensity of the chip, the mismatch between computation layer shapes and the available computation resource significantly limits the utilization of the chip. Driven by this observation, prior works discuss spatial
Externí odkaz:
http://arxiv.org/abs/2401.10417
Autor:
Ji, Shixin, Yang, Zhuoping, Chen, Xingzhen, Cahoon, Stephen, Hu, Jingtong, Shi, Yiyu, Jones, Alex K., Zhou, Peipei
Embodied carbon has been widely reported as a significant component in the full system lifecycle of various computing systems' green house gas emissions. Many efforts have been undertaken to quantify the elements that comprise this embodied carbon, f
Externí odkaz:
http://arxiv.org/abs/2401.06270
Autor:
Yang, Zhuoping, Ji, Shixin, Chen, Xingzhen, Zhuang, Jinming, Zhang, Weifeng, Jani, Dharmesh, Zhou, Peipei
Fast-evolving artificial intelligence (AI) algorithms such as large language models have been driving the ever-increasing computing demands in today's data centers. Heterogeneous computing with domain-specific architectures (DSAs) brings many opportu
Externí odkaz:
http://arxiv.org/abs/2311.16417
Autor:
Zhou, Peipei, Zhuang, Jinming, Cahoon, Stephen, Tang, Yue, Yang, Zhuoping, Chen, Xingzhen, Shi, Yiyu, Hu, Jingtong, Jones, Alex K.
There is a growing call for greater amounts of increasingly agile computational power for edge and cloud infrastructure to serve the computationally complex needs of ubiquitous computing devices. Thus, an important challenge is addressing the holisti
Externí odkaz:
http://arxiv.org/abs/2312.02991
Autor:
Qin, Ruiyang, Xia, Jun, Jia, Zhenge, Jiang, Meng, Abbasi, Ahmed, Zhou, Peipei, Hu, Jingtong, Shi, Yiyu
After a large language model (LLM) is deployed on edge devices, it is desirable for these devices to learn from user-generated conversation data to generate user-specific and personalized responses in real-time. However, user-generated data usually c
Externí odkaz:
http://arxiv.org/abs/2311.12275
Arbitrary-precision integer multiplication is the core kernel of many applications in simulation, cryptography, etc. Existing acceleration of arbitrary-precision integer multiplication includes CPUs, GPUs, FPGAs, and ASICs. Among these accelerators,
Externí odkaz:
http://arxiv.org/abs/2309.12275
As the increasing complexity of Neural Network(NN) models leads to high demands for computation, AMD introduces a heterogeneous programmable system-on-chip (SoC), i.e., Versal ACAP architectures featured with programmable logic (PL), CPUs, and dedica
Externí odkaz:
http://arxiv.org/abs/2305.18698
Autor:
Zhuang, Jinming, Lau, Jason, Ye, Hanchen, Yang, Zhuoping, Du, Yubo, Lo, Jack, Denolf, Kristof, Neuendorffer, Stephen, Jones, Alex, Hu, Jingtong, Chen, Deming, Cong, Jason, Zhou, Peipei
Dense matrix multiply (MM) serves as one of the most heavily used kernels in deep learning applications. To cope with the high computation demands of these applications, heterogeneous architectures featuring both FPGA and dedicated ASIC accelerators
Externí odkaz:
http://arxiv.org/abs/2301.02359