9 citations · 17 across the 5 of their papers we have counts for
8 papers
FSL-HDnn: A 5.7 TOPS/W End-to-end Few-shot Learning Classifier Accelerator with Feature Extraction and Hyperdimensional Computing
Haichao Yang, Chang Eun Song, Weihong Xu +6
This paper introduces FSL-HDnn, an energy-efficient accelerator that implements the end-to-end pipeline of feature extraction, classification, and on-chip few-shot learning (FSL) t…
HDReason: Algorithm-Hardware Codesign for Hyperdimensional Knowledge Graph Reasoning
Hanning Chen, Yang Ni, Ali Zakeri +7
In recent times, a plethora of hardware accelerators have been put forth for graph learning applications such as vertex classification and graph classification. However, previous w…
HD-Bind: Encoding of Molecular Structure with Low Precision, Hyperdimensional Binary Representations
Derek Jones, Jonathan E. Allen, Xiaohua Zhang +5
Publicly available collections of drug-like molecules have grown to comprise 10s of billions of possibilities in recent history due to advances in chemical synthesis. Traditional m…
Streaming Encoding Algorithms for Scalable Hyperdimensional Computing
Anthony Thomas, Behnam Khaleghi, Gopi Krishna Jha +5
Hyperdimensional computing (HDC) is a paradigm for data representation and learning originating in computational neuroscience. HDC represents data as high-dimensional, low-precisio…
SHEARer: Highly-Efficient Hyperdimensional Computing by Software-Hardware Enabled Multifold Approximation
Behnam Khaleghi, Sahand Salamat, Anthony Thomas +3
Hyperdimensional computing (HD) is an emerging paradigm for machine learning based on the evidence that the brain computes on high-dimensional, distributed, representations of data…
Prive-HD: Privacy-Preserved Hyperdimensional Computing
Behnam Khaleghi, Mohsen Imani, Tajana Rosing
The privacy of data is a major challenge in machine learning as a trained model may expose sensitive information of the enclosed dataset. Besides, the limited computation capabilit…
FPGA Energy Efficiency by Leveraging Thermal Margin
Behnam Khaleghi, Sahand Salamat, Mohsen Imani +1
Cutting edge FPGAs are not energy efficient as conventionally presumed to be, and therefore, aggressive power-saving techniques have become imperative. The clock rate of an FPGA-ma…
Workload-Aware Opportunistic Energy Efficiency in Multi-FPGA Platforms
Sahand Salamat, Behnam Khaleghi, Mohsen Imani +1
The continuous growth of big data applications with high computational and scalability demands has resulted in increasing popularity of cloud computing. Optimizing the performance…