Hierarchical Hyperdimensional Computing for Energy Efficient Classification

Imani, Mohsen; Huang, Chenyu; Kong, Deqian; Rosing, Tajana

doi:10.1109/dac.2018.8465708

Cited by 29 publications

(23 citation statements)

References 7 publications

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“…There exist other methods that have used hyperdimensional techniques to perform recognition (Imani et al, 2017 ) and classification (Moon et al, 2013 ; Rahimi et al, 2016 ; Imani et al, 2018 ; Kleyko et al, 2018 ). As with HAP (Mitrokhin et al, 2019 ), there have been other attempts to perform feature and decision fusion (Jimenez et al, 1999 ) or paradigms that can operate with minuscule amounts of resources (Rahimi et al, 2017 ).…”

Section: Related Workmentioning

confidence: 99%

Symbolic Representation and Learning With Hyperdimensional Computing

et al. 2020

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It has been proposed that machine learning techniques can benefit from symbolic representations and reasoning systems. We describe a method in which the two can be combined in a natural and direct way by use of hyperdimensional vectors and hyperdimensional computing. By using hashing neural networks to produce binary vector representations of images, we show how hyperdimensional vectors can be constructed such that vector-symbolic inference arises naturally out of their output. We design the Hyperdimensional Inference Layer (HIL) to facilitate this process and evaluate its performance compared to baseline hashing networks. In addition to this, we show that separate network outputs can directly be fused at the vector symbolic level within HILs to improve performance and robustness of the overall model. Furthermore, to the best of our knowledge, this is the first instance in which meaningful hyperdimensional representations of images are created on real data, while still maintaining hyperdimensionality.

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Section: Related Workmentioning

confidence: 99%

Symbolic Representation and Learning With Hyperdimensional Computing

et al. 2020

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“…Work in [34] proposed an encoding method to map and classify biosignal sensory data in high dimensional space. Work in [12] proposed a general encoding module that maps feature vectors into high-dimensional space while keeping most of the original data. Prior work also designed diferent training framework to enable sparsity and quantization in HD computing [11,21].…”

Section: Related Workmentioning

confidence: 99%

“…HDC builds upon a well-deined set of operations with random HDC vectors, making HDC extremely robust in the presence of failures, and ofers a complete computational paradigm that is easily applied to learning problems [25]. Prior work has shown the suitability of HDC for various applications like activity recognition, face detection, language recognition, image classiication, etc [38,12,36,28,14,17,13].…”

Section: Introductionmentioning

confidence: 99%

Store-n-Learn: Classification and Clustering with Hyperdimensional Computing across Flash Hierarchy

Gupta

Khaleghi

Salamat

et al. 2022

ACM Trans. Embed. Comput. Syst.

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Processing large amounts of data, specially in learning algorithms, poses a challenge for current embedded computing systems. Hyperdimensional computing (HDC) is a brain-inspired computing paradigm that works with high-dimensional vectors, hypervectors . HDC replaces several complex learning computations with bitwise and simpler arithmetic operations at the expense of an increased amount of data due to mapping the data into high-dimensional space. These hypervectors, more often than not, can’t be stored in memory, resulting in long data transfers from storage. In this paper, we propose Store-n-Learn, an in-storage computing (ISC) solution that performs HDC classification and clustering by implementing encoding, training, retraining, and inference across the flash hierarchy. To hide the latency of training and enable efficient computation, we introduce the concept of batching in HDC. We also present on-chip acceleration for HDC encoding in flash planes. This enables us to exploit the high parallelism provided by the flash hierarchy and encode multiple data points in parallel in both batched and non-batched fashion. Store-n-Learn also implements a single top-level FPGA accelerator with novel implementations for HDC classification training, retraining, inference, and clustering on the encoded data. Our evaluation over ten popular datasets shows that Store-n-Learn is on average 222 × (543 ×) faster than CPU and 10.6 × (7.3 ×) faster than the state-of-the-art ISC solution, INSIDER for HDC classification (clustering).

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“…Brain-inspired hyperdimensional (HD) computing emulates cognition tasks by computing with hypervectors. MHD, a multi-encoder hierarchical classifier was proposed, which enables HD to take full advantages of multiple encoders without increasing the cost of classification (Imani et al, 2018).…”

Section: Brain-inspired Computingmentioning

confidence: 99%

Progress and Challenges of Neuroscience and Brain-inspired Artificial Intelligence

Wang¹,

Alexander²

2019

Neuroscience International

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Neuroscience and brain-inspired artificial intelligence are significant research areas. Many countries have launched brain-related projects in which neuroscience and brain-inspired artificial intelligence are major targeted areas to increase national interests and enhance their strength in key areas such as military and homeland security in the competitive global world. Methods, emerging technologies, and progress in neuroscience and brain-inspired artificial intelligence are introduced in this paper that specifically include brain-inspired computing, brain association graph, brain networks, the connectome, brain reconstruction, imaging technologies used for the brain, chips and devices inspired by the human brain, brain-computer interface or brain-machine interfaces, cyborg, neuro-robotics, and quantum robotics. Challenges in some of the topics are also presented and discussed.

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Hierarchical Hyperdimensional Computing for Energy Efficient Classification

Cited by 29 publications

References 7 publications

Symbolic Representation and Learning With Hyperdimensional Computing

Symbolic Representation and Learning With Hyperdimensional Computing

Store-n-Learn: Classification and Clustering with Hyperdimensional Computing across Flash Hierarchy

Progress and Challenges of Neuroscience and Brain-inspired Artificial Intelligence

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