HyDREA: Towards More Robust and Efficient Machine Learning Systems with Hyperdimensional Computing
Justin Morris1,2,a, Kazim Ergun1,b, Behnam Khaleghi1,c, Mohsen Imani3, Baris Aksanli2 and Tajana Rosing1,d
1University of California San Diego, La Jolla, CA 92093, USA
ajustinmorris@ucsd.edu
bkerguna@ucsd.edu
cbkhalegha@ucsd.edu
dtajana@ucsd.edu@ucsd.edu
2San Diego State University, San Diego, CA 92182, USA
baksanli@sdsu.edu
3University of California Irvine, Irvine, CA 92697, USA
m.imani@uci.edu
ABSTRACT
Today’s systems, especially in the age of federated learning, rely on sending all the data to the cloud, and then use complex algorithms, such as Deep Neural Networks, which require billions of parameters and many hours to train a model. In contrast, the human brain can do much of this learning effortlessly. Hyperdimensional (HD) Computing aims to mimic the behavior of the human brain by utilizing high dimensional representations. This leads to various desirable properties that other Machine Learning (ML) algorithms lack such as: robustness to noise in the system and simple, highly parallel operations. In this paper, we propose HyDREA, a HD computing system that is Robust, Efficient, and Accurate. To evaluate the feasibility of HyDREA in a federated learning environment with wireless communication noise, we utilize NS-3, a popular network simulator that models a real world environment with wireless communication noise. We found that HyDREA is 48 × more robust to noise than other comparable ML algorithms. We additionally propose a Processing-in-Memory (PIM) architecture that adaptively changes the bitwidth of the model based on the signal to noise ratio (SNR) of the incoming sample to maintain the robustness of the HD model while achieving high accuracy and energy efficiency. Our results indicate that our proposed system loses less than 1% classification accuracy, even in scenarios with an SNR of 6.64. Our PIM architecture is also able to achieve 255× better energy efficiency and speed up execution time by 28× compared to the baseline PIM architecture.
