Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userAn Intelligent Material with Chemical Pathway Networks
117
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
A new type of material with embedded intelligence, namely intelligent plasma, is introduced. Such new material exhibits programmable chemical pathway networks resembling artificial neural networks. As a Markov process of chemistry, the chemical pathway network can be customized and thus the intelligent plasmas can be programmed to make their own decisions to react to the dynamic external and internal conditions. It finally can accomplish complex missions without any external controls from the humans while relying on its preprogrammed chemical network topology before the mission. To that end, only basic data input and readings are required without any external controls during the mission. The approach to if conditions and while loops of the programmable intelligent plasmas are also discussed with examples of applications including automatic workflows, and signal processing.
rate research
Read More
We consider how to generate chemical reaction networks (CRNs) from functional specifications. We propose a two-stage approach that combines synthesis by satisfiability modulo theories and Markov chain Monte Carlo based optimisation. First, we identify candidate CRNs that have the possibility to produce correct computations for a given finite set of inputs. We then optimise the reaction rates of each CRN using a combination of stochastic search techniques applied to the chemical master equation, simultaneously improving the of correct behaviour and ruling out spurious solutions. In addition, we use techniques from continuous time Markov chain theory to study the expected termination time for each CRN. We illustrate our approach by identifying CRNs for majority decision-making and division computation, which includes the identification of both known and unknown networks.
Present day computers expend orders of magnitude more computational resources to perform various cognitive and perception related tasks that humans routinely perform everyday. This has recently resulted in a seismic shift in the field of computation where research efforts are being directed to develop a neurocomputer that attempts to mimic the human brain by nanoelectronic components and thereby harness its efficiency in recognition problems. Bridging the gap between neuroscience and nanoelectronics, this paper attempts to provide a review of the recent developments in the field of spintronic device based neuromorphic computing. Description of various spin-transfer torque mechanisms that can be potentially utilized for realizing device structures mimicking neural and synaptic functionalities is provided. A cross-layer perspective extending from the device to the circuit and system level is presented to envision the design of an All-Spin neuromorphic processor enabled with on-chip learning functionalities. Device-circuit-algorithm co-simulation framework calibrated to experimental results suggest that such All-Spin neuromorphic systems can potentially achieve almost two orders of magnitude energy improvement in comparison to state-of-the-art CMOS implementations.
Monolithic three-dimensional integration of memory and logic circuits could dramatically improve performance and energy efficiency of computing systems. Some conventional and emerging memories are suitable for vertical integration, including highly scalable metal-oxide resistive switching devices (memristors), yet integration of logic circuits proves to be much more challenging. Here we demonstrate memory and logic functionality in a monolithic three-dimensional circuit by adapting recently proposed memristor-based stateful material implication logic. Though such logic has been already implemented with a variety of memory devices, prohibitively large device variability in the most prospective memristor-based circuits has limited experimental demonstrations to simple gates and just a few cycles of operations. By developing a low-temperature, low-variability fabrication process, and modifying the original circuit to increase its robustness to device imperfections, we experimentally show, for the first time, reliable multi-cycle multi-gate material implication logic operation within a three-dimensional stack of monolithically integrated memristors. The direct data manipulation in three dimensions enables extremely compact and high-throughput logic-in-memory computing and, remarkably, presents a viable solution for the Feynman grand challenge of implementing an 8-bit adder at the nanoscale.
Reconfigurable intelligent surface (RIS) has become a promising technology for enhancing the reliability of wireless communications, which is capable of reflecting the desired signals through appropriate phase shifts. However, the intended signals that impinge upon an RIS are often mixed with interfering signals, which are usually dynamic and unknown. In particular, the received signal-to-interference-plus-noise ratio (SINR) may be degraded by the signals reflected from the RISs that originate from non-intended users. To tackle this issue, we introduce the concept of intelligent spectrum learning (ISL), which uses an appropriately trained convolutional neural network (CNN) at the RIS controller to help the RISs infer the interfering signals directly from the incident signals. By capitalizing on the ISL, a distributed control algorithm is proposed to maximize the received SINR by dynamically configuring the active/inactive binary status of the RIS elements. Simulation results validate the performance improvement offered by deep learning and demonstrate the superiority of the proposed ISL-aided approach.
The explosive growth of data and its related energy consumption is pushing the need to develop energy-efficient brain-inspired schemes and materials for data processing and storage. Here, we demonstrate experimentally that Co/Pt films can be used as artificial synapses by manipulating their magnetization state using circularly-polarized ultrashort optical pulses at room temperature. We also show an efficient implementation of supervised perceptron learning on an opto-magnetic neural network, built from such magnetic synapses. Importantly, we demonstrate that the optimization of synaptic weights can be achieved using a global feedback mechanism, such that the learning does not rely on external storage or additional optimization schemes. These results suggest there is high potential for realizing artificial neural networks using optically-controlled magnetization in technologically relevant materials, that can learn not only fast but also energy-efficient.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
