An official website of the United States government
Hyperdimensional computing (HDC) is a novel computational paradigm that operates on long-dimensional vectors known as hypervectors. The hypervectors are constructed as long bit-streams and form the basic building blocks of HDC systems. In HDC, hypervectors are generated from scalar values without considering bit significance. HDC is efficient and robust for various data processing applications, especially computer vision tasks. To construct HDC models for vision applications, the current state-of-the-art practice utilizes two parameters for data encoding: pixel intensity and pixel position. However, the intensity and position information embedded in high-dimensional vectors are generally not generated dynamically in the HDC models. Consequently, the optimal design of hypervectors with high model accuracy requires powerful computing platforms for training. A more efficient approach is to generate hypervectors dynamically during the training phase. To this aim, this work uses low-discrepancy sequences to generate intensity hypervectors, while avoiding position hypervectors. Doing so eliminates the multiplication step in vector encoding, resulting in a power-efficient HDC system. For the first time in the literature, our proposed approach employs lightweight vector generators utilizing unary bit-streams for efficient encoding of data instead of using conventional comparator-based generators.
more »
« less
Achieving software-equivalent accuracy for hyperdimensional computing with ferroelectric-based in-memory computing
Abstract Hyperdimensional computing (HDC) is a brain-inspired computational framework that relies on long hypervectors (HVs) for learning. In HDC, computational operations consist of simple manipulations of hypervectors and can be incredibly memory-intensive. In-memory computing (IMC) can greatly improve the efficiency of HDC by reducing data movement in the system. Most existing IMC implementations of HDC are limited to binary precision which inhibits the ability to match software-equivalent accuracies. Moreover, memory arrays used in IMC are restricted in size and cannot immediately support the direct associative search of large binary HVs (a ubiquitous operation, often over 10,000+ dimensions) required to achieve acceptable accuracies. We present a multi-bit IMC system for HDC using ferroelectric field-effect transistors (FeFETs) that simultaneously achieves software-equivalent-accuracies, reduces the dimensionality of the HDC system, and improves energy consumption by 826x and latency by 30x when compared to a GPU baseline. Furthermore, for the first time, we experimentally demonstrate multi-bit, array-level content-addressable memory (CAM) operations with FeFETs. We also present a scalable and efficient architecture based on CAMs which supports the associative search of large HVs. Furthermore, we study the effects of device, circuit, and architectural-level non-idealities on application-level accuracy with HDC.
more »
« less
- Award ID(s):
- 2127780
- PAR ID:
- 10423723
- Date Published:
- Journal Name:
- Scientific Reports
- Volume:
- 12
- Issue:
- 1
- ISSN:
- 2045-2322
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Hyperdimensional computing (HDC) is a novel computational paradigm that operates on long-dimensional vectors known as hypervectors. The hypervectors are constructed as long bit-streams and form the basic building blocks of HDC systems. In HDC, hypervectors are generated from scalar values without considering bit significance. HDC is efficient and robust for various data processing applications, especially computer vision tasks. To construct HDC models for vision applications, the current state-of-the-art practice utilizes two parameters for data encoding: pixel intensity and pixel position. However, the intensity and position information embedded in high-dimensional vectors are generally not generated dynamically in the HDC models. Consequently, the optimal design of hypervectors with high model accuracy requires powerful computing platforms for training. A more efficient approach is to generate hypervectors dynamically during the training phase. To this aim, this work uses low-discrepancy sequences to generate intensity hypervectors, while avoiding position hypervectors. Doing so eliminates the multiplication step in vector encoding, resulting in a power-efficient HDC system. For the first time in the literature, our proposed approach employs lightweight vector generators utilizing unary bit-streams for efficient encoding of data instead of using conventional comparator-based generators.more » « less
-
Hyperdimensional vector processing is a nascent computing approach that mimics the brain structure and offers lightweight, robust, and efficient hardware solutions for different learning and cognitive tasks. For image recognition and classification, hyperdimensional computing (HDC) utilizes the intensity values of captured images and the positions of image pixels. Traditional HDC systems represent the intensity and positions with binary hypervectors of 1K–10K dimensions. The intensity hypervectors are cross-correlated for closer values and uncorrelated for distant values in the intensity range. The position hypervectors are pseudo-random binary vectors generated iteratively for the best classification performance. In this study, we propose a radically new approach for encoding image data in HDC systems. Position hypervectors are no longer needed by encoding pixel intensities using a deterministic approach based on quasi-random sequences. The proposed approach significantly reduces the number of operations by eliminating the position hypervectors and the multiplication operations in the HDC system. Additionally, we suggest a hybrid technique for generating hypervectors by combining two deterministic sequences, achieving higher classification accuracy. Our experimental results show up to 102× reduction in runtime and significant memory-usage savings with improved accuracy compared to a baseline HDC system with conventional hypervector encoding.more » « less
-
Emerging brain-inspired hyperdimensional computing (HDC) algorithms are vulnerable to timing and soft errors in associative memory used to store high-dimensional data representations. Such errors can significantly degrade HDC performance. A key challenge is error correction after an error in computation is detected. This work presents two novel error resilience frameworks for hyperdimensional computing systems. The first, called the checksum hypervector encoding (CHE) framework, relies on creation of a single additional hypervector that is a checksum of all the class hypervectors of the HDC system. For error resilience, elementwise validation of the checksum property is performed and those elements across all class vectors for which the property fails are removed from consideration. For an HDC system with K class hypervectors of dimension D, the second cross-hypervector clustering (CHC) framework clusters D, Kdimensional vectors consisting of the i-th element of each of the K HDC class hypervectors, 1 ≤ i ≤ K. Statistical properties of these vector clusters are checked prior to each hypervector query and all the elements of all K-dimensional vectors corresponding to statistical outlier vectors are removed as before. The choice of which framework to use is dictated by the complexity of the dataset to classify. Up to three orders of magnitude better resilience to errors than the state-of-the-art across multiple HDC high-dimensional encoding (representation) systems is demonstrated.more » « less
-
Emerging brain-inspired hyperdimensional computing (HDC) algorithms are vulnerable to timing and soft errors in associative memory used to store high-dimensional data representations. Such errors can significantly degrade HDC performance. A key challenge is error correction after an error in computation is detected. This work presents two novel error resilience frameworks for hyperdimensional computing systems. The first, called the checksum hypervector encoding (CHE) framework, relies on creation of a single additional hypervector that is a checksum of all the class hypervectors of the HDC system. For error resilience, elementwise validation of the checksum property is performed and those elements across all class vectors for which the property fails are removed from consideration. For an HDC system with K class hypervectors of dimension D, the second cross-hypervector clustering (CHC) framework clusters D, K-dimensional vectors consisting of the i-th element of each of the K HDC class hypervectors, 1 ≤ i ≤ K. Statistical properties of these vector clusters are checked prior to each hypervector query and all the elements of all K-dimensional vectors corresponding to statistical outlier vectors are removed as before. The choice of which framework to use is dictated by the complexity of the dataset to classify. Up to three orders of magnitude better resilience to errors than the state-of-the-art across multiple HDC high-dimensional encoding (representation) systems is demonstrated.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1038/s41598-022-23116-w
