2 citations · 2 across the 7 of their papers we have counts for
5 papers · 1 filter
NSFlow: An End-to-End FPGA Framework with Scalable Dataflow Architecture for Neuro-Symbolic AI
Hanchen Yang, Zishen Wan, Ritik Raj +5
Neuro-Symbolic AI (NSAI) is an emerging paradigm that integrates neural networks with symbolic reasoning to enhance the transparency, reasoning capabilities, and data efficiency of…
CogSys: Efficient and Scalable Neurosymbolic Cognition System via Algorithm-Hardware Co-Design
Zishen Wan, Hanchen Yang, Ritik Raj +4
Neurosymbolic AI is an emerging compositional paradigm that fuses neural learning with symbolic reasoning to enhance the transparency, interpretability, and trustworthiness of AI.…
Towards Efficient Neuro-Symbolic AI: From Workload Characterization to Hardware Architecture
Zishen Wan, Che-Kai Liu, Hanchen Yang +13
The remarkable advancements in artificial intelligence (AI), primarily driven by deep neural networks, are facing challenges surrounding unsustainable computational trajectories, l…
RASA: Efficient Register-Aware Systolic Array Matrix Engine for CPU
Geonhwa Jeong, Eric Qin, Ananda Samajdar +4
As AI-based applications become pervasive, CPU vendors are starting to incorporate matrix engines within the datapath to boost efficiency. Systolic arrays have been the premier arc…
Architecture, Dataflow and Physical Design Implications of 3D-ICs for DNN-Accelerators
Jan Moritz Joseph, Ananda Samajdar, Lingjun Zhu +4
The everlasting demand for higher computing power for deep neural networks (DNNs) drives the development of parallel computing architectures. 3D integration, in which chips are int…