Memorization is an essential functionality that enables today's machine learning algorithms to provide a high quality of learning and reasoning for each prediction. Memorization gives algorithms prior knowledge to keep the context and define confidence for their decision. Unfortunately, the existing deep learning algorithms have a weak and nontransparent notion of memorization. Brain-inspired HyperDimensional Computing (HDC) is introduced as a model of human memory. Therefore, it mimics several important functionalities of the brain memory by operating with a vector that is computationally tractable and mathematically rigorous in describing human cognition. In this manuscript, we introduce a brain-inspired system that represents HDC memorization capability over a graph of relations. We propose GrapHD, hyperdimensional memorization that represents graph-based information in high-dimensional space. GrapHD defines an encoding method representing complex graph structure while supporting both weighted and unweighted graphs. Our encoder spreads the information of all nodes and edges across into a full holistic representation so that no component is more responsible for storing any piece of information than another. Then, GrapHD defines several important cognitive functionalities over the encoded memory graph. These operations include memory reconstruction, information retrieval, graph matching, and shortest path. Our extensive evaluation shows that GrapHD: (1) significantly enhances learning capability by giving the notion of short/long term memorization to learning algorithms, (2) enables cognitive computing and reasoning over memorization graph, and (3) enables holographic brain-like computation with substantial robustness to noise and failure.

/pdf/graphd-graph-based-hyperdimensional-memorization-for-brain-2h3xnqye.pdf

GrapHD: Graph-Based Hyperdimensional Memorization for Brain-Like Cognitive Learning

This two-part comprehensive survey is devoted to a computing framework most commonly known under the names Hyperdimensional Computing and Vector Symbolic Architectures (HDC/VSA). Both names refer to a family of computational models that use high-dimensional distributed representations and rely on the algebraic properties of their key operations to incorporate the advantages of structured symbolic representations and vector distributed representations. Notable models in the HDC/VSA family are Tensor Product Representations, Holographic Reduced Representations, Multiply-Add-Permute, Binary Spatter Codes, and Sparse Binary Distributed Representations but there are other models too. HDC/VSA is a highly interdisciplinary area with connections to computer science, electrical engineering, artificial intelligence, mathematics, and cognitive science. This fact makes it challenging to create a thorough overview of the area. However, due to a surge of new researchers joining the area in recent years, the necessity for a comprehensive survey of the area has become extremely important. Therefore, amongst other aspects of the area, this Part I surveys important aspects such as: known computational models of HDC/VSA and transformations of various input data types to high-dimensional distributed representations. Part II of this survey is devoted to applications, cognitive computing and architectures, as well as directions for future work. The survey is written to be useful for both newcomers and practitioners.

A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part I: Models and Data Transformations.

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-the-art pattern matching algorithm running on GeForce RTX 3060 Ti GPU (state-of-the-art PIM accelerator).

BioHD: an efficient genome sequence search platform using HyperDimensional memorization

A real-time electrical load forecasting and unsupervised anomaly detection framework

The manufacturing industry is currently witnessing a paradigm shift with the unprecedented adoption of industrial robots, and machine vision is a key perception technology that enables these robots to perform precise operations in unstructured environments. However, the sensitivity of conventional vision sensors to lighting conditions and high-speed motion sets a limitation on the reliability and work-rate of production lines. Neuromorphic vision is a recent technology with the potential to address the challenges of conventional vision with its high temporal resolution, low latency, and wide dynamic range. In this paper and for the first time, we propose a novel neuromorphic vision based controller for robotic machining applications to enable faster and more reliable operation, and present a complete robotic system capable of performing drilling tasks with sub-millimeter accuracy. Our proposed system localizes the target workpiece in 3D using two perception stages that we developed specifically for the asynchronous output of neuromorphic cameras. The first stage performs multi-view reconstruction for an initial estimate of the workpiece’s pose, and the second stage refines this estimate for a local region of the workpiece using circular hole detection. The robot then precisely positions the drilling end-effector and drills the target holes on the workpiece using a combined position-based and image-based visual servoing approach. The proposed solution is validated experimentally for drilling nutplate holes on workpieces placed arbitrarily in an unstructured environment with uncontrolled lighting. Experimental results prove the effectiveness of our solution with maximum positional errors of less than 0.2 mm, and demonstrate that the use of neuromorphic vision overcomes the lighting and speed limitations of conventional cameras. The findings of this paper identify neuromorphic vision as a promising technology that can expedite and robustify robotic manufacturing processes in line with the requirements of the fourth industrial revolution. • We present a neuromorphic visual controller approach for precise robotic machining. • We devise an event-based 3D reconstruction method for the localization of workpieces. • We develop an event-based method for the detection and tracking of circular objects. • We preform experiments that validate the proposed method in a robotic drilling setup. • The presented system can perform drilling tasks with sub-millimeter precision. • Results demonstrate the performance and robustness advantages of neuromorphic vision. 

Neuromorphic Vision Based Control for the Precise Positioning of Robotic Drilling Systems

Brain-inspired architectures are gaining increased attention, especially for edge devices to perform cognitive tasks utilizing its limited energy budget and computing resources. Hyperdimensional computing (HDC) paradigm is an emerging framework inspired by an abstract representation of neuronal circuits’ attributes in the human brain. That includes a fully holographic random representation, high-dimension vectors representing data, and robustness to uncertainty. The basic HDC pipeline consists of an encoding, training and comparison stages. The encoding algorithm maps different representations of inputs into a single class and stores them in the associative memory (AM) throughout the training stage. Later, during the inference stage, the similarity is computed between the query vector, which is encoded using the same encoding model, and the stored classes in the AM. HDC has shown promising results for 1D applications using less power, and lower latency than state-of-the-art digital neural networks (DNN). While in 2D applications, convolutional neural network (CNN) still achieves higher classification accuracy at the expense of more computations. In this paper, a comprehensive study on the HDC paradigm, main algorithms, and its implementation is presented. Moreover, the main state-of-the-art HDC architectures for 1D and 2D applications are highlighted. The article also analyzes two competing paradigms, namely, HDC and CNN, in terms of accuracy, complexity, and the number of operations. The paper concluded by highlighting challenges and recommendations for future directions on the HDC framework.

/pdf/hyper-dimensional-computing-challenges-and-opportunities-for-374omxp1um.pdf

Hyper-Dimensional Computing Challenges and Opportunities for AI Applications

Content addressable memory (CAM) for search and match operations demands high speed and low power for near real-time decision-making across many critical domains. Resistive RAM (RRAM)-based in-memory computing has high potential in realizing an efficient static CAM for artificial intelligence tasks, especially on resource-constrained platforms. This paper presents an XNOR-based RRAM-CAM with a time-domain analog adder for efficient winning class computation. The CAM compares two operands, one voltage and the second one resistance, and outputs a voltage proportional to the similarity between the input query and the pre-stored patterns. Processing the summation of the output similarity voltages in the time-domain helps avoid voltage saturation, variation, and noise dominating the analog voltage-based computing. After that, to determine the winning class among the multiple classes, a digital realization is utilized to consider the class with the longest pulse width as the winning class. As a demonstrator, hyperdimensional computing for efficient MNIST classification is considered. The proposed design uses 65 nm CMOS foundry technology and realistic data for RRAM with total area of 0.0077 mm2, consumes 13.6 pJ of energy per 1 k query within 10 ns clock cycle. It shows a reduction of ~ 31 × in area and ~ 3 × in energy consumption compared to fully digital ASIC implementation using 65 nm foundry technology. The proposed design exhibits a remarkable reduction in area and energy compared to two of the state-of-the-art RRAM designs.

/pdf/rram-based-cam-combined-with-time-domain-circuits-for-4kavd89uvq.pdf

RRAM-based CAM combined with time-domain circuits for hyperdimensional computing.

Memristor-based implementations promises efficient in-memory computing architectures. Hence, it has been extensively utilized as multiply-and-add accelerator engines in signal processing and artificial intelligence applications. Hyperdimensional computing (HDC) paradigm is an encouraging brain-inspired computational framework that performs computations on hyperdimensional vectors. The encoding operation in HDC takes about 80% of the execution time and consists of multi-plication, addition and shifting. In this paper, a reconfigurable memristor array is used to implement in-memory shifting and addition to the seeds of the hyperdimensional vectors. The presented scheme fuses the circular shifting with summation operations. This is the first work to introduce such scheme and it provides savings in time, power and area compared to traditional computations and other crossbar approaches that performs separate operations on the crossbar. Spice simulation of the proposed scheme using 65nm foundry has been used to verify the functionality.

Fused RRAM-Based Shift-Add Architecture for Efficient Hyperdimensional Computing Paradigm

In recent years, robotic sorting is widely used in the industry, which is driven by necessity and opportunity. In this paper, a novel neuromorphic vision-based tactile sensing approach for robotic sorting application is proposed. This approach has low latency and low power consumption when compared to conventional vision-based tactile sensing techniques. Two Machine Learning (ML) methods, namely, Support Vector Machine (SVM) and Dynamic Time Warping-K Nearest Neighbor (DTW-KNN), are developed to classify material hardness, object size, and grasping force. An Event-Based Object Grasping (EBOG) experimental setup is developed to acquire datasets, where 243 experiments are produced to train the proposed classifiers. Based on predictions of the classifiers, objects can be automatically sorted. If the prediction accuracy is below a certain threshold, the gripper re-adjusts and re-grasps until reaching a proper grasp. The proposed ML method achieves good prediction accuracy, which shows the effectiveness and the applicability of the proposed approach. The experimental results show that the developed SVM model outperforms the DTW-KNN model in term of accuracy and efficiency for real time contact-level classification.

https://www.mdpi.com/1424-8220/20/17/4724/pdf

Neuromorphic Vision Based Contact-Level Classification in Robotic Grasping Applications.

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Hyper-Dimensional Computing Challenges and Opportunities for AI Applications

RRAM-based CAM combined with time-domain circuits for hyperdimensional computing.

Fused RRAM-Based Shift-Add Architecture for Efficient Hyperdimensional Computing Paradigm

Neuromorphic Vision Based Contact-Level Classification in Robotic Grasping Applications.