Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Linear Approximations01:23

Linear Approximations

For a differentiable function of two variables, linear approximation estimates values near a known point by replacing the curved surface with its tangent plane. Consider the function\begin{equation*}f(x,y)=x^2+3y^2\end{equation*}near the point (2, 1). The exact value at this point is f(2, 1) = 22 + 3(1)2 = 4 + 3 = 7.The linear approximation of f(x, y)) near (a, b) is\begin{equation*}L(x,y)=f(a,b)+f_x(a,b)(x-a)+f_y(a,b)(y-b)\end{equation*}First, compute the partial derivatives: fx(x, y) = 2x and...
Optimization Problems01:26

Optimization Problems

Optimization problems often involve identifying maximum or minimum values under specific constraints. A well-known example is determining the longest horizontal pipe that can be moved around a right-angled corner, where a 3-meter-wide hallway meets a 2-meter-wide hallway. This scenario, common in architectural design and industrial transport, can be understood conceptually through geometric and trigonometric reasoning.To visualize the problem, consider the pipe as a straight line that touches...
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length, the...
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear.
Application of Linearization and Approximation01:29

Application of Linearization and Approximation

A drone flying through complex terrain often relies on more than one sensing method to estimate small changes in altitude. Along with direct measurements, air pressure provides a useful indirect indicator of vertical movement. Atmospheric pressure decreases as altitude increases, and this relationship is commonly described using an exponential model. Although accurate, converting pressure measurements into altitude values requires calculations that are too complex to perform repeatedly during...
Linearization and Approximation01:26

Linearization and Approximation

Linearization is a mathematical technique used to approximate complex, nonlinear functions with simpler linear models in the vicinity of a chosen reference point. The method is based on the idea that, although a function may be difficult to evaluate exactly, its behavior near a specific input value can often be closely approximated by the tangent line at that point. This approach is particularly useful when small deviations from a known value are involved.Consider the square root function, for...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

The IMAP Observatory Overview.

Space science reviews·2026
Same author

Precision Measurement of Neutrino Oscillation Parameters with 10 Years of Data from the NOvA Experiment.

Physical review letters·2026
Same author

A participatory photovoice investigation of community assets, barriers, and opportunities to curb the opioid epidemic.

Journal of substance use and addiction treatment·2025
Same author

Dual-Baseline Search for Active-to-Sterile Neutrino Oscillations in NOvA.

Physical review letters·2025
Same author

In-vessel design of a two-color heterodyne laser interferometer system for SPARC.

The Review of scientific instruments·2024
Same author

Search for CP-Violating Neutrino Nonstandard Interactions with the NOvA Experiment.

Physical review letters·2024

Related Experiment Videos

Backpropagation in linear arrays-a performance analysis and optimization.

D Naylor1, S Jones, D Myers

  • 1Dept. of Electron. and Electr. Eng., Loughborough Univ. of Technol.

IEEE Transactions on Neural Networks
|January 1, 1995
PubMed
Summary
This summary is machine-generated.

Optimizing neural network design for hardware is crucial. This study shows that network structure and mapping significantly impact performance, not just size, for efficient image processing.

Related Experiment Videos

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Hardware Architecture

Background:

  • Neural networks are essential for image processing, often requiring specialized parallel hardware for real-time applications.
  • Linear systolic arrays are a common architecture for high-speed neural network processing.
  • Implementing multi-layer neural networks on these architectures presents design complexities.

Purpose of the Study:

  • To investigate the impact of neural network structure and hardware mapping on performance.
  • To identify optimal strategies for structuring neural networks to enhance throughput and reduce latency.
  • To improve the efficiency of hardware resource utilization in neural network processors.

Main Methods:

  • Analysis of neural network design principles in the context of linear systolic arrays.
  • Evaluation of different network mapping strategies onto the HANNIBAL neural network processor.
  • Performance benchmarking focusing on learning/recall times, throughput, and latency.

Main Results:

  • The smallest neural network is not always the most efficient for learning or recall.
  • The way a neural network is mapped to hardware significantly affects application performance and resource efficiency.
  • Specific structuring techniques were identified to optimize network performance.

Conclusions:

  • Network size alone does not determine optimal performance; structure and mapping are critical.
  • Effective hardware mapping is key to maximizing throughput, minimizing latency, and utilizing resources efficiently.
  • The HANNIBAL processor validated the proposed techniques for optimizing neural network hardware implementations.