Scalable edge-based hyperdimensional learning system with brain-like neural adaptation

Zou, Zhuowen; Kim, Yeseong; Imani, Farhad; Alimohamadi, Haleh; Cammarota, Rosario; Imani, Mohsen

doi:10.1145/3458817.3480958

Cited by 48 publications

(18 citation statements)

References 46 publications

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“…In this paper, we also interchangeably use binary and bipolar if applicable. Note that the input vector F = {𝑓 1 , 𝑓 2 , ..., 𝑓 𝑁 } can represent raw features or extracted features (using, e.g., neural networks and random feature map [40]).…”

Section: Brain-inspired Hdc Classifiersmentioning

confidence: 99%

“…Concretely, the training process of an HDC classifier is extremely simple -simply averaging over the hypervectors of labeled training samples to derive the corresponding class hypervectors. Although some heuristic techniques (e.g., re-training and regeneration [11,18,40]) have been recently added, the existing HDC training process still lacks rigorousness and heavily relies on a trial-and-error process without systematic guidance as in the realm of DNNs. In fact, even a well-defined loss function is lacking in the training of HDC classifiers.…”

Section: Introductionmentioning

confidence: 99%

“…The study[40] applies random feature map for feature extraction[32] and then directly binarizes the extracted features as the encoded hypervector. Nonetheless, we can also use value hypervectors and feature hypervectors to encode the non-binary features extracted via random feature map, and this can preserve more information in the extracted features than direct binarization.…”

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confidence: 99%

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A Brain-Inspired Low-Dimensional Computing Classifier for Inference on Tiny Devices

Duan¹,

Xu²,

Ren³

2022

Preprint

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By mimicking brain-like cognition and exploiting parallelism, hyperdimensional computing (HDC) classifiers have been emerging as a lightweight framework to achieve efficient on-device inference. Nonetheless, they have two fundamental drawbacks -heuristic training process and ultra-high dimension -which result in suboptimal inference accuracy and large model sizes beyond the capability of tiny devices with stringent resource constraints. In this paper, we address these fundamental drawbacks and propose a lowdimensional computing (LDC) alternative. Specifically, by mapping our LDC classifier into an equivalent neural network, we optimize our model using a principled training approach. Most importantly, we can improve the inference accuracy while successfully reducing the ultra-high dimension of existing HDC models by orders of magnitude (e.g., 8000 vs. 4/64). We run experiments to evaluate our LDC classifier by considering different datasets for inference on tiny devices, and also implement different models on an FPGA platform for acceleration. The results highlight that our LDC classifier offers an overwhelming advantage over the existing brain-inspired HDC models and is particularly suitable for inference on tiny devices.

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Section: Brain-inspired Hdc Classifiersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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A Brain-Inspired Low-Dimensional Computing Classifier for Inference on Tiny Devices

Duan¹,

Xu²,

Ren³

2022

Preprint

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“…To achieve real-time performance with high energy efficiency and robustness, our approach redesigns learning algorithms using strategies that closely model the human brain at an abstract level. We exploit Hyper-Dimensional Computing (HDC) as an alternative computational model that mimics important brain functionalities toward high-efficiency and noise-tolerant computation (Kanerva, 2009 ; Rahimi et al, 2016b ; Pale et al, 2021 , 2022 ; Zou et al, 2021 ). HDC supports operators that emulate the behavior of associative memory and enables higher cognitive functionalities (Gayler, 2004 ; Kanerva, 2009 ; Poduval et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

EventHD: Robust and efficient hyperdimensional learning with neuromorphic sensor

et al. 2022

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Brain-inspired computing models have shown great potential to outperform today's deep learning solutions in terms of robustness and energy efficiency. Particularly, Hyper-Dimensional Computing (HDC) has shown promising results in enabling efficient and robust cognitive learning. In this study, we exploit HDC as an alternative computational model that mimics important brain functionalities toward high-efficiency and noise-tolerant neuromorphic computing. We present EventHD, an end-to-end learning framework based on HDC for robust, efficient learning from neuromorphic sensors. We first introduce a spatial and temporal encoding scheme to map event-based neuromorphic data into high-dimensional space. Then, we leverage HDC mathematics to support learning and cognitive tasks over encoded data, such as information association and memorization. EventHD also provides a notion of confidence for each prediction, thus enabling self-learning from unlabeled data. We evaluate EventHD efficiency over data collected from Dynamic Vision Sensor (DVS) sensors. Our results indicate that EventHD can provide online learning and cognitive support while operating over raw DVS data without using the costly preprocessing step. In terms of efficiency, EventHD provides 14.2× faster and 19.8× higher energy efficiency than state-of-the-art learning algorithms while improving the computational robustness by 5.9×.

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“…Hyper-dimensional computing (HDC): HDC effectively mimics several important functionalities of the human memory and allows energy-efficient computation based on its massively parallel computation flow [35,37,[44][45][46]. HDC is motivated by an observation that the human brain operates on a robust high-dimensional representation of data due to the large size of brain circuits [47][48][49].…”

Section: Introductionmentioning

confidence: 99%

BioHD

Zou

Chen

Poduval

et al. 2022

Proceedings of the 49th Annual International Symposium on Computer Architecture

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In this paper, we propose BioHD, a novel genomic sequence searching platform based on Hyper-Dimensional Computing (HDC) for hardware-friendly computation. BioHD transforms inherent sequential processes of genome matching to highly-parallelizable computation tasks. We exploit HDC memorization to encode and represent the genome sequences using high-dimensional vectors. Then, it combines the genome sequences to generate an HDC reference library. During the sequence searching, BioHD performs exact or approximate similarity check of an encoded query with the HDC reference library. Our framework simplifies the required sequence matching operations while introducing a statistical model to control the alignment quality. To get actual advantage from BioHD inherent robustness and parallelism, we design a processing in-memory (PIM) architecture with massive parallelism and compatible with the existing crossbar memory. Our PIM architecture supports all essential BioHD operations natively in memory with minimal modification on the array. We evaluate BioHD accuracy and efficiency on a wide range of genomics data, including COVID-19 databases. Our results indicate that PIM provides 102.8× and 116.1× (9.3× and 13.2×) speedup and energy efficiency compared to the state-of-theart pattern matching algorithm running on GeForce RTX 3060 Ti GPU (state-of-the-art PIM accelerator).

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Scalable edge-based hyperdimensional learning system with brain-like neural adaptation

Cited by 48 publications

References 46 publications

A Brain-Inspired Low-Dimensional Computing Classifier for Inference on Tiny Devices

A Brain-Inspired Low-Dimensional Computing Classifier for Inference on Tiny Devices

EventHD: Robust and efficient hyperdimensional learning with neuromorphic sensor

BioHD

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