What devices does the inference engine allow developers to target?
Answers from top 9 papers
More filters
| Papers (9) | Insight |
|---|---|
11 May 2010 40 Citations | That is, inference engine can be changed without rewriting the rules for engine-specific syntax. |
| We may be on the threshold of achieving the old dream of a universal inference engine. | |
26 Mar 1986 10 Citations | The inference engine is general and can accommodate general modifications and additions to the knowledge base. |
13 Jul 2015 143 Citations | Monte Carlo simulations demonstrate an exceptional robustness of the inference engine with respect to device variations and other issues. |
| Secondly, the inference engine is the only mandatory technological infrastructure which means that no additional implementation effort is needed. | |
02 Nov 1994 20 Citations | The first goal, responsiveness, is best accommodated by limiting the number of attributes that the inference engine takes into consideration. |
| In addition the inference engine generates a set of predictions that can be used to control the interpretation strategy so as to make the processing of new images more efficient. | |
| As a result, the application programmer can implicitly and transparently take full advantage of the facilities of the inference engine using simple, known data base manipulation language statements. | |
40 Citations | Finally, we demonstrate that the type of inference engine used has a substantial effect on the absolute level of predicted performance. |
Related Questions
What is inference control in computer security?4 answersIn computer security, inference control refers to the measures taken to prevent the deduction of sensitive information from non-sensitive data through data dependencies. Existing solutions focus on either restricting queries that may lead to leakage or protecting against exact reconstruction of sensitive data. A novel security model called full deniability has been proposed, where any inference about sensitive data from non-sensitive data is considered a leakage, enhancing security. This model aims to efficiently implement inference control by hiding minimal additional non-sensitive cells while scaling well with database size and sensitive data. In the realm of networked dynamical systems, inference control extends to protecting control mechanism secrets from stealthy attacks, emphasizing the need to safeguard control process vulnerabilities beyond traditional data privacy measures.
What are some of the most popular AI tools?4 answersArtificial Intelligence (AI) tools have become increasingly popular in various fields. Some of the most popular AI tools include knowledge-based systems, fuzzy logic, automatic knowledge acquisition, neural networks, genetic algorithms, case-based reasoning, and ambient intelligence ^[Sanders]. These tools have been widely used in sensor systems, expert system development, and research in fields such as ophthalmology and osteoporosis management ^[Richer] ^[Dimai]. AI tools like ChatGPT and DALL-E have gained attention for their ability to understand and process natural language, generate human-like responses, and create lifelike digital images from text prompts ^[MAHESH K M] ^[Gabora]. These tools have shown promise in various applications, including information retrieval, patient triage, symptom assessment, data analysis, literature review, and collaboration among researchers ^[MAHESH K M] ^[Dimai]. However, it is important to interpret the results of AI tools with caution and ensure high-quality clinical research in the field ^[Dimai].
How can FPGAs be used to accelerate AI inference?5 answersFPGAs can be used to accelerate AI inference by leveraging their programmability and parallel processing capabilities. The use of FPGAs allows for the implementation of custom architectures and optimization of AI models for specific applications. For example, the Xilinx Versal ACAP platform has been compared to MPSoC FPGA for Deep Learning applications, and it was found that the custom architecture of an image super-resolution model developed for Versal ACAP improved latency by 4.5x compared to the implementation derived from the Vitis AI framework. Additionally, a software framework has been developed by SLAC to deploy ML structures on FPGAs at the edge of the data chain, close to the instrumentation. This framework optimizes data flow between layers and supports full re-deployment of weights and biases without requiring re-synthesis after training. Another approach is the use of symbolic regression (SR) combined with FPGA-based machine learning inference, which allows for optimizing the performance-resource tradeoff and reducing computational resources needed for critical tasks.
What is inference?5 answersInference refers to the process of deriving information or conclusions that are not explicitly stated in the text but can be inferred based on the reader's knowledge and understanding. It involves going beyond the literal meaning of the text and making connections or drawing conclusions based on contextual cues and background knowledge. Inference can involve both the derivation of new information and the activation of existing knowledge. It is an important component of discourse understanding and plays a role in everyday language processing. Inference is different from implicature, which refers to speaker meaning that goes beyond what is explicitly said. In statistical investigations, inference is the final step where decisions or predictions are made based on data and assumptions. In cognitive processes, inference involves coming to believe something on the basis of existing beliefs, either through rational causation or mistakenly through deviant causation.
How to draw inference in reading literature?5 answersDrawing inferences in reading literature involves using prior knowledge and information from the text to make predictions and understand the deeper meaning of the text. Proficient readers use their prior knowledge and the information they have gathered from the text to make predictions about what might happen next. In a study conducted with EFL learners, it was found that learners who were able to draw inferences performed significantly better in a recall test for reading comprehension. Expert think-alouds conducted with student literary readers revealed that having domain-specific knowledge about literary conventions, such as rules of notice and rules of signification, helped in constructing interpretive inferences. Attention to language mediated the effect, suggesting that language plays a role in leveraging student engagement in literary interpretation.
What advantages do you get by running inference on a server instead of distributing the model to all your clients?14 answers