Drug-target binding affinity prediction plays a key role in the early stage of drug discovery. Numerous experimental and data-driven approaches have been developed for predicting drug-target binding affinity. However, experimental methods highly rely on the limited structural-related information from drug-target pairs, domain knowledge, and time-consuming assays. On the other hand, learning-based methods have shown an acceptable prediction performance. However, most of them utilize several simple and complex types of proteins and drug compounds data, ranging from the protein sequences to the topology of a graph representation of drug compounds, employing multiple deep neural networks for encoding and feature extraction, and so, leads to the computational overheads. In this study, we propose a unified measure for protein sequence encoding, named BiComp, which provides compression-based and evolutionary-related features from the protein sequences. Specifically, we employ Normalized Compression Distance and Smith-Waterman measures for capturing complementary information from the algorithmic information theory and biological domains, respectively. We utilize the proposed measure to encode the input proteins feeding a new deep neural network-based method for drug-target binding affinity prediction, named BiComp-DTA. BiComp-DTA is evaluated utilizing four benchmark datasets for drug-target binding affinity prediction. Compared to the state-of-the-art methods, which employ complex models for protein encoding and feature extraction, BiComp-DTA provides superior efficiency in terms of accuracy, runtime, and the number of trainable parameters. The latter achievement facilitates execution of BiComp-DTA on a normal desktop computer in a fast fashion. As a comparative study, we evaluate BiComp's efficiency against its components for drug-target binding affinity prediction. The results have shown superior accuracy of BiComp due to the orthogonality and complementary nature of Smith-Waterman and Normalized Compression Distance measures for protein sequences. Such a protein sequence encoding provides efficient representation with no need for multiple sources of information, deep domain knowledge, and complex neural networks.

In response to the imperfections of current sequence alignment methods, originated from the inherent serialism within their corresponding electrical systems, a few optical approaches for biological data comparison have been proposed recently. However, due to their low performance, raised from their inefficient coding scheme, this paper presents a novel all-optical high-throughput method for aligning DNA, RNA, and protein sequences, named HELIOS. The HELIOS method employs highly sophisticated operations to locate character matches, single or multiple mutations, and single or multiple indels within various biological sequences. On the other hand, the HELIOS optical architecture exploits high-speed processing and operational parallelism in optics, by adopting wavelength and polarization of optical beams. For evaluation, the functionality and accuracy of the HELIOS method are approved through behavioral and optical simulation studies, while its complexity and performance are estimated through analytical computation. The accuracy evaluations indicate that the HELIOS method achieves a precise pairwise alignment of two sequences, highly similar to those of Smith-Waterman, Needleman-Wunsch, BLAST, MUSCLE, ClustalW, ClustalΩ, T-Coffee, Kalign, and MAFFT. According to our performance evaluations, the HELIOS optical architecture outperforms all alternative electrical and optical algorithms in terms of processing time and memory requirement, relying on its highly sophisticated method and optical architecture. Moreover, the employed compact coding scheme highly escalates the number of input characters, and hence, it offers reduced time and space complexities, compared to the electrical and optical alternatives. It makes the HELIOS method and optical architecture highly applicable for biomedical applications.

Performance of a data center is a function of three features; bandwidth, latency, and reliability. By adopting optical technology in data center network, bandwidth increment, in addition to reduction of transmission latency and power consumption, is achieved. Unfortunately, fault tolerance of the optical networks has raised less attention so far. So in this paper, we propose a fault-tolerant, scalable, and high-performance optical architecture built upon previously proposed O-TF network, with the goal of redundancy optimization and reducing the minimum number of wavelength channels required for non-blocking functionality of the network. Moreover, reducing network diameter, in O-FTF network compared to O-TF and Wavecube networks, has led to higher network performance at the presence of network failures.

In this paper, we propose a new architecture for nanophotonic Networks on Chip (NoC), named 2D-HERT, which consists of optical data and control planes. The proposed data plane is built upon a new topology and all-optical switches that passively route optical data streams based on their wavelengths. Utilizing wavelength routing method, the proposed deterministic routing algorithm, and Wavelength Division Multiplexing (WDM) technique, the proposed data plane eliminates the need for optical resource reservation at the intermediate nodes. For resolving end-point contention, we propose an all-optical request-grant arbitration architecture which reduces optical losses compared to the alternative arbitration schemes. By performing a series of simulations, we study the efficiency of the proposed architecture, its power and energy consumption, and the data transmission delay. Moreover, we compare the proposed architecture with electrical NoCs and alternative optical NoCs under various synthetic traffic patterns. Averaged across different traffic patterns, 2D-HERT reduces data transmission delay by 24, 15, 18, 4, and 70 percent and achieves average per-packet power reduction of 58, 47, 52, 45, and 95 percent over Phastlane, Firefly, Corona architecture, $(\lambda)$-router, and electrical Torus, respectively.

This article proposes a scalable wavelength-routed optical Network on Chip (NoC) based on the Spidergon topology, named Power-efficient Scalable Wavelength-routed Network-on-chip (PeSWaN). The key idea of the proposed all-optical architecture is the utilization of per-receiver wavelengths in the data network to prevent network contention and the adoption of per-sender wavelengths in the control network to avoid end-point contention. By performing a series of simulations, we study the efficiency of the proposed architecture, its power and energy consumption, and the data transmission delay. Moreover, we compare the proposed architecture with electrical NoCs and alternative ONoC architectures under various traffic patterns.

This paper proposes a nanophotonic Network-on-Chip architecture based on the traditional Cube-Connected Cycles topology (CCC), which is named as ONC3. We also suggest a contention-free quasi-Dimension-Order-Routing algorithm for the proposed structure. Compared to the previous 2D layouts, our novel scheme lessens the crosstalk parameter of the insertion loss and consequently, the power consumption. Besides, the router structure is area-efficient. On the other hand, optical destination checking supersedes electrical resource reservation, with utilizing passive wavelength routing method and Wavelength Division Multiplexing scheme, simultaneously. The efficiency of the proposed architecture, in terms of the essential NoC parameters such as delay and power consumption, is investigated through simulation-based experiments and is compared with electrical CCC and Torus topologies, as well as the existing ONoCs such as the optical crossbar and ?-router in various synthetic traffic patterns.

This paper proposes a novel topology for optical Network on Chip (NoC) architectures with the key advantages of regularity, vertex symmetry, scalability to large scale networks, constant node degree, and simplicity. Moreover, we propose a minimal deterministic routing algorithm for the proposed topology which leads to small and simple photonic routers. Built upon our novel network topology, we present a scalable all-optical NoC, referred to as 2D-HERT, which offers passive routing of optical data streams based on their wavelengths. Utilizing wavelength routing method along with Wavelength Division Multiplexing technique, our proposed optical NoC eliminates the need for electrical resource reservation. We compare performance of the proposed architecture against electrical NoCs and alternative all-optical on-chip architectures under various synthetic traffic patterns. Averaging through different traffic patterns, achieves average perpacket power reduction of 53%, 45%, and 95% over optical crossbar, λ-router, and electrical Torus, respectively.

This paper proposes a novel all-optical router as a building block for a scalable wavelength-switched optical NoC. The proposed optical router, named as AOR, performs passive routing of optical data streams based on their wavelengths. Utilizing wavelength routing method, AOR eliminates the need for electrical resource reservation and the corresponding latency and area overheads. Taking advantage of Wavelength Division Multiplexing (WDM) technique, the proposed architecture is capable of data multicasting, concurrent with unicast data transmission, with high bandwidth and low power dissipation, without imposing noticeable area and latency overheads. Comparing AOR against previously proposed optical routers, we deduce that the proposed router architecture reduces optical insertion loss, electrical power consumption, and number of micro rings, and also improves scalability of the on-chip network.

In this paper, we extract analytical models for data transmission delay, power consumption, and energy dissipation of optical and traditional NoCs. Utilizing extracted models, we compare optical NoC with electrical one for varying values of link length and degree of multiplexing and calculate lower bound limit on the optical link length below which optical on-chip network loses its efficiency. Based on this constraint, we propose a novel hierarchical on-chip network architecture, named as H2NoC, which benefits from optical transmissions in large scale SoCs and overcomes the scalability problem resulted from lower bound limit on the optical link length. Performing a series of simulation-based experiments, we study efficiency of H2NoC along with its power and energy consumption and data transmission delay. Through experimental results, we show that despite slight delay increment in H2NoC compared to non-hierarchical ONoC, later architecture reduces power and energy dissipation of the network.

We propose a new architecture for on-chip routing of optical packets. The proposed infrastructure, referred to as CONoC, facilitates the development of an all-optical on-chip network and alleviates the role of electrical NoCs. As the first step for designing an all-optical NoC, CONoC resolves packet congestions optically and does not use electrical methods. Utilizing wavelength routing method, wavelength division multiplexing, and path reconfiguration capability in CONoC leads to a contention-free architecture. This architectural advantage along with simple and small photonic router architecture results in simple electrical transactions, reduced setup latency, and high transmission capacity. Moreover, we discuss the proper topology for on-chip optical interconnects. Performing a series of simulation-based experiments, we study the efficiency of CONoC along with its power and energy consumption and data transmission delay.

NoC is an efficient on-chip communication architecture for SoC architectures. It enables integration of a large number of computational and storage blocks on a single chip. NoCs have tackled the SoCs disadvantages and are scalable. In this paper, we compare two popular NoC topologies, i.e., mesh and torus, in terms of different figures of merit e.g., latency, power consumption, and power/throughput ratio under different routing algorithms and two common traffic models, uniform and hotspot. To the best of our knowledge, this is the first effort in comparing mesh and torus topologies under different routing algorithms and traffic models with respect to their performance and power consumption.