More Like this

1. Cross-Layer Design of Vector-Symbolic Computing: Bridging Cognition and Brain-Inspired Hardware Acceleration

   https://doi.org/10.1145/3807784  

   Du, Shuting; Ibrahim, Mohamed; Wan, Zishen; Zheng, Luqi; Zhao, Boheng; Fan, Zhenkun; Liu, Che-kai; Krishna, Tushar; Raychowdhury, Arijit; Li, Haitong (April 2026, ACM Transactions on Embedded Computing Systems)

   Vector Symbolic Architectures (VSAs), also known as hyperdimensional (HD) computing, are increasingly deployed in cognitive applications due to their simple and efficient operations. The widespread adoption has, in turn, spurred the development of a diverse set of hardware solutions that optimize VSA performance for embedded and edge AI systems. Despite these advances, there remains a lack of comprehensive, unified discussion on the co-design and co-evolution of VSA algorithms and hardware. This survey aims to bridge that gap by linking theoretical, software-level explorations with efficient hardware architectures and emerging technology fabrics for VSAs, providing co-design insights that are accessible to both algorithm and hardware communities. First, we introduce the principles of vector-symbolic computing, including its core mathematical operations and learning paradigms. Second, we provide an in-depth discussion on hardware technologies for VSAs, analyzing analog, mixed-signal, and digital circuit design styles. We compare hardware implementations of VSAs by carrying out detailed analysis of their performance characteristics and tradeoffs, from which we distill design guidelines that are applicable across arbitrary VSA formulations. Third, we discuss a methodology for cross-layer design of VSAs that identifies synergies across layers and explores key ingredients for hardware/software co-design of VSAs. Finally, as a concrete case study of this methodology, we present an in-memory computing hardware design for VSA-based hierarchical cognition, illustrating how the proposed co-design principles translate into efficient architectures. The paper concludes with a discussion of open research challenges and opportunities for future explorations. 

   more » « less
2. Design of a Quadratic, Antagonistic, Cable-Driven, Variable Stiffness Actuator

   https://doi.org/10.1115/1.4050104  

   Moore, Ryan; Schimmels, Joseph M (June 2021, Journal of Mechanisms and Robotics)

   Antagonistically actuated variable stiffness actuators (VSAs) take inspiration from biological muscle structures to control both the stiffness and positioning of a joint. This paper presents the design of an elastic mechanism that utilizes a cable running through a set of three pulleys to displace a linear spring, yielding quadratic spring behavior in each actuator. A joint antagonistically actuated by two such mechanisms yields a linear relationship between force and deflection from a selectable equilibrium position. A quasi-static model is used to optimize the mechanism. Testing of the fabricated prototype yielded a good match to the desired elastic behavior. 

   more » « less
3. Variable Binding for Sparse Distributed Representations: Theory and Applications

   https://doi.org/10.1109/TNNLS.2021.3105949  

   Frady, Edward Paxon; Kleyko, Denis; Sommer, Friedrich T. (September 2021, IEEE Transactions on Neural Networks and Learning Systems)

   null (Ed.)

   Variable binding is a cornerstone of symbolic reasoning and cognition. But how binding can be implemented in connectionist models has puzzled neuroscientists, cognitive psychologists, and neural network researchers for many decades. One type of connectionist model that naturally includes a binding operation is vector symbolic architectures (VSAs). In contrast to other proposals for variable binding, the binding operation in VSAs is dimensionality-preserving, which enables representing complex hierarchical data structures, such as trees, while avoiding a combinatoric expansion of dimensionality. Classical VSAs encode symbols by dense randomized vectors, in which information is distributed throughout the entire neuron population. By contrast, in the brain, features are encoded more locally, by the activity of single neurons or small groups of neurons, often forming sparse vectors of neural activation. Following Laiho et al. (2015), we explore symbolic reasoning with a special case of sparse distributed representations. Using techniques from compressed sensing, we first show that variable binding in classical VSAs is mathematically equivalent to tensor product binding between sparse feature vectors, another well-known binding operation which increases dimensionality. This theoretical result motivates us to study two dimensionality-preserving binding methods that include a reduction of the tensor matrix into a single sparse vector. One binding method for general sparse vectors uses random projections, the other, block-local circular convolution, is defined for sparse vectors with block structure, sparse block-codes. Our experiments reveal that block-local circular convolution binding has ideal properties, whereas random projection based binding also works, but is lossy. We demonstrate in example applications that a VSA with block-local circular convolution and sparse block-codes reaches similar performance as classical VSAs. Finally, we discuss our results in the context of neuroscience and neural networks. 

   more » « less
4. Design, Modeling, Evaluation, and Control of a Compact Discrete Variable Stiffness Actuator

   https://doi.org/10.1115/1.4069904  

   Hussain, Irfan; Turki_Almansoori, Mariam; Tamaizi, Khaled; Albalasie, Ahmad; Niu, Zhenwei; Gan, Dongming (February 2026, Journal of Mechanisms and Robotics)

   Abstract This article introduces a novel compact discrete variable stiffness actuator, distinguished by its design topology enabling compactness and independent stiffness adjustment regardless of position, without relying on complex stiffness tuning mechanisms. The compactness is achieved through a rotary spring module comprising six linear springs. Variation in stiffness is attained by adjusting the number of engaged elastic elements (springs) using linear actuators. The article comprehensively covers the design, modeling, prototyping, and control of the actuator. A mathematical model, encompassing stiffness and dynamic aspects of the system, is developed and analyzed. Robust controllers are devised to ensure stability across different stiffness levels and under varying loads. Simulation and experimental findings provide empirical validation of the effectiveness of this innovative compact actuation system, underscoring its suitability for diverse applications across sectors such as manufacturing, healthcare, and assistive robotics. 

   more » « less
5. Convex Optimization for Spring Design in Series Elastic Actuators: From Theory to Practice

   https://doi.org/10.1109/IROS51168.2021.9636427  

   Bolivar-Nieto, Edgar A.; Thomas, Gray C.; Rouse, Elliott; Gregg, Robert D. (September 2021, 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   Natural dynamics, nonlinear optimization, and, more recently, convex optimization are available methods for stiffness design of energy-efficient series elastic actuators. Natural dynamics and general nonlinear optimization only work for a limited set of load kinetics and kinematics, cannot guarantee convergence to a global optimum, or depend on initial conditions to the numerical solver. Convex programs alleviate these limitations and allow a global solution in polynomial time, which is useful when the space of optimization variables grows (e.g., when designing optimal nonlinear springs or co-designing spring, controller, and reference trajectories). Our previous work introduced the stiffness design of series elastic actuators via convex optimization when the transmission dynamics are negligible, which is an assumption that applies mostly in theory or when the actuator uses a direct or quasi-direct drive. In this work, we extend our analysis to include friction at the transmission. Coulomb friction at the transmission results in a non-convex expression for the energy dissipated as heat, but we illustrate a convex approximation for stiffness design. We experimentally validated our framework using a series elastic actuator with specifications similar to the knee joint of the Open Source Leg, an open-source robotic knee-ankle prosthesis. 

   more » « less