Synthesis Tools Research Articles

Expanding hardware accelerator system design space exploration with gem5-SALAMv2

With the prevalence of hardware accelerators as an integral part of the modern systems on chip (SoCs), the ability to model accelerators quickly and accurately within the system in which it operates is critical. This paper presents gem5-SALAMv2 as a novel system architecture for LLVM-based modeling and simulation of custom hardware accelerators integrated into the gem5 framework. It overcomes the inherent limitations of state-of-the-art trace-based pre-register-transfer level (RTL) simulators by offering a truly “execute-in-execute” LLVM-based model. It enables scalable modeling of multiple dynamically interacting accelerators with full-system simulation support. To create long-term sustainable expansion compatible with the gem5 system framework, gem5-SALAM offers a general-purpose and modular communication interface and memory hierarchy integrated into the gem5 ecosystem, streamlining designing and modeling accelerators for new and emerging applications. gem5-SALAMv2 expands upon the framework established in gem5-SALAMv1 with improved LLVM-based elaboration and simulation, improved and more extensible system integration, and new automations to simplify rapid prototyping and design space exploration. 11Conference Paper Extension: This work extends the work presented in gem5-SALAM: A System Architecture for LLVM-based Accelerator Modeling from MICRO 2020 (Rogers et al., 2020). This work expands on the aforementioned work by revamping the gem5-SALAM internals to provide more robust and extensible simulations, introducing new automation tools for expanding and simplifying design space exploration, and demonstrates the new capabilities of gem5-SALAMv2 by exploring multiple configurations of simple neural network architectures.Validation on the MachSuite (Reagen et al., 2014) benchmarks presents a timing estimation error of less than 1% against the Vivado High-Level Synthesis (HLS) tool. Results also show less than a 4% area and power estimation error against Synopsys Design Compiler. Additionally, system validation against implementations on an Ultrascale+ ZCU102 shows an average end-to-end timing error of less than 2%. Lastly, we demonstrate the upgraded capabilities of gem5-SALAMv2 by exploring accelerator platforms for two deep neural networks, LeNet5 and MobileNetv2. In these explorations, we demonstrate how gem5-SALAMv2 can simulate such systems and guide architectural optimizations for these types of accelerator-rich architectures. 22The most up-to-date version of gem5-SALAMv2 is available at https://github.com/TeCSAR-UNCC/gem5-SALAM..

Exploiting multivariate network meta-analysis: A calibrated Bayesian composite likelihood inference.

Multivariate network meta-analysis has emerged as a powerful tool in evidence synthesis by incorporating multiple outcomes and treatments. Despite its advantages, this method comes with methodological challenges, such as the issue of unreported within-study correlations among treatments and outcomes, which potentially lead to misleading conclusions. In this paper, we proposed a calibrated Bayesian composite likelihood approach to overcome this limitation. The proposed method eliminated the need to specify a full likelihood function while allowing for the unavailability of within-study correlations among treatments and outcomes. Additionally, we developed a hybrid Gibbs sampler algorithm along with the Open-Faced Sandwich post-sampling adjustment to enable robust posterior inference. Through comprehensive simulation studies, we demonstrated that the proposed approach yielded unbiased estimates while maintaining coverage probabilities close to the nominal level. Furthermore, we implemented the proposed method on two real-world network meta-analysis datasets; one comparing treatment procedures for the root coverage and another comparing treatments for anaemia in chronic kidney disease patients.

An Approach to Enhancing Manufacturing Throughput Capability through System Design

<div>This article presents a new approach that integrates and balances cycle time and reliability considerations during system design to enhance manufacturing system capability, with direct applicability to automotive and high-volume manufacturing. The method addresses system throughput capability in three steps without complex mathematical modeling. The steps are: (1) managing workload cycle times, (2) considering real-world operational availability, and (3) achieving capability balance. Through analytical estimation and discrete event simulation of an example manufacturing system, this approach’s application shows a substantial increase in throughput capacity compared to a traditional workload-based design. This throughput-centric design approach, serving as a valuable tool for manufacturing system synthesis aimed at maximizing throughput, can apply to volume production systems to enhance throughput capacity through system design.</div>

Chemoenzymatic Synthesis of 2‐Aryl Thiazolines from 4‐Hydroxybenzaldehydes Using Vanillyl Alcohol Oxidases

AbstractNitrogen heterocycles are commonly found in bioactive natural products and drugs. However, the biocatalytic tools for nitrogen heterocycle synthesis are limited. Herein, we report the discovery of vanillyl alcohol oxidases (VAOs) as efficient biocatalysts for the one‐pot synthesis of 2‐aryl thiazolines from various 4‐hydroxybenzaldehydes and aminothiols. The wild‐type biocatalyst features a broad scope of 4‐hydroxybenzaldehydes. Though the scope of aminothiols is limited, it could be improved via semi‐rational protein engineering, generating a variant to produce previously inaccessible cysteine‐derived bioactive 2‐aryl thiazolines using the wild‐type VAO. Benefiting from the derivatizable functional groups in the enzymatic products, we further chemically modified these products to expand the chemical space, offering a new chemoenzymatic strategy for the green and efficient synthesis of structurally diverse 2‐aryl‐thiazoline derivatives to prompt their use in drug discovery and catalysis.

Chemoenzymatic Synthesis of 2-Aryl Thiazolines from 4-Hydroxybenzaldehydes Using Vanillyl Alcohol Oxidases.

Nitrogen heterocycles are commonly found in bioactive natural products and drugs. However, the biocatalytic tools for nitrogen heterocycle synthesis are limited. Herein, we report the discovery of vanillyl alcohol oxidases (VAOs) as efficient biocatalysts for the one-pot synthesis of 2-aryl thiazolines from various 4-hydroxybenzaldehydes and aminothiols. The wild-type biocatalyst features a broad scope of 4-hydroxybenzaldehydes. Though the scope of aminothiols is limited, it could be improved via semi-rational protein engineering, generating a variant to produce previously inaccessible cysteine-derived bioactive 2-aryl thiazolines using the wild-type VAO. Benefiting from the derivatizable functional groups in the enzymatic products, we further chemically modified these products to expand the chemical space, offering a new chemoenzymatic strategy for the green and efficient synthesis of structurally diverse 2-aryl-thiazoline derivatives to prompt their use in drug discovery and catalysis.

Performance of two large language models for data extraction in evidence synthesis.

Accurate data extraction is a key component of evidence synthesis and critical to valid results. The advent of publicly available large language models (LLMs) has generated interest in these tools for evidence synthesis and created uncertainty about the choice of LLM. We compare the performance of two widely available LLMs (Claude 2 and GPT-4) for extracting pre-specified data elements from 10 published articles included in a previously completed systematic review. We use prompts and full study PDFs to compare the outputs from the browser versions of Claude 2 and GPT-4. GPT-4 required use of a third-party plugin to upload and parse PDFs. Accuracy was high for Claude 2 (96.3%). The accuracy of GPT-4 with the plug-in was lower (68.8%); however, most of the errors were due to the plug-in. Both LLMs correctly recognized when prespecified data elements were missing from the source PDF and generated correct information for data elements that were not reported explicitly in the articles. A secondary analysis demonstrated that, when provided selected text from the PDFs, Claude 2 and GPT-4 accurately extracted 98.7% and 100% of the data elements, respectively. Limitations include the narrow scope of the study PDFs used, that prompt development was completed using only Claude 2, and that we cannot guarantee the open-source articles were not used to train the LLMs. This study highlights the potential for LLMs to revolutionize data extraction but underscores the importance of accurate PDF parsing. For now, it remains essential for a human investigator to validate LLM extractions.

Using scalable computer vision to automate high-throughput semiconductor characterization

High-throughput materials synthesis methods, crucial for discovering novel functional materials, face a bottleneck in property characterization. These high-throughput synthesis tools produce 104 samples per hour using ink-based deposition while most characterization methods are either slow (conventional rates of 101 samples per hour) or rigid (e.g., designed for standard thin films), resulting in a bottleneck. To address this, we propose automated characterization (autocharacterization) tools that leverage adaptive computer vision for an 85x faster throughput compared to non-automated workflows. Our tools include a generalizable composition mapping tool and two scalable autocharacterization algorithms that: (1) autonomously compute the band gaps of 200 compositions in 6 minutes, and (2) autonomously compute the environmental stability of 200 compositions in 20 minutes, achieving 98.5% and 96.9% accuracy, respectively, when benchmarked against domain expert manual evaluation. These tools, demonstrated on the formamidinium (FA) and methylammonium (MA) mixed-cation perovskite system FA1−xMAxPbI3, 0 ≤ x ≤ 1, significantly accelerate the characterization process, synchronizing it closer to the rate of high-throughput synthesis.

Biocatalytic stereocontrolled head-to-tail cyclizations of unbiased terpenes as a tool in chemoenzymatic synthesis

Terpene synthesis stands at the forefront of modern synthetic chemistry and represents the state-of-the-art in the chemist’s toolbox. Notwithstanding, these endeavors are inherently tied to the current availability of natural cyclic building blocks. Addressing this limitation, the stereocontrolled cyclization of abundant unbiased linear terpenes emerges as a valuable tool, which is still difficult to achieve with chemical catalysts. In this study, we showcase the remarkable capabilities of squalene-hopene cyclases (SHCs) in the chemoenzymatic synthesis of head-to-tail-fused terpenes. By combining engineered SHCs and a practical reaction setup, we generate ten chiral scaffolds with >99% ee and de, at up to decagram scale. Our mechanistic insights suggest how cyclodextrin encapsulation of terpenes may influence the performance of the membrane-bound enzyme. Moreover, we transform the chiral templates to valuable (mero)-terpenes using interdisciplinary synthetic methods, including a catalytic ring-contraction of enol-ethers facilitated by cooperative iodine/lipase catalysis.

Click chemistry beyond metal-catalyzed cycloaddition as a remarkable tool for green chemical synthesis of antifungal medications.

Click chemistry is widely used for the efficient synthesis of 1,4-disubstituted-1,2,3-triazole, a well-known scaffold with widespread biological activity in the pharmaceutical sciences. In recent years, this magic ring has attracted the attention of scientists for its potential in designing and synthesizing new antifungal agents. Despite scientific and medical advances, fungal infections still account for more than 1.5 million deaths globally per year, especially in people with compromised immune function. This increasing trend is definitely related to a raise in the incidence of fungal infections and prevalence of antifungal drug resistance. In this condition, an urgent need for new alternative antifungals is undeniable. By focusing on the main aspects of reaction conditions in click chemistry, this review was conducted to classify antifungal 1,4-disubstituted-1,2,3-triazole hybrids based on their chemical structures and introduce the most effective triazole antifungal derivatives. It was notable that in all reactions studied, Cu(I) catalysts generated insitu by the reduction in Cu(II) salts or used copper(I) salts directly, as well as mixed solvents of t-BuOH/H2O and DMF/H2O had most application in the synthesis of triazole ring. The most effective antifungal activity was also observed in fluconazole analogs containing 1,2,3-triazole moiety and benzo-fused five/six-membered heterocyclic conjugates with a 1,2,3-triazole ring, even with better activity than fluconazole. The findings of structure-activity relationship and molecular docking of antifungal derivatives synthesized with copper-catalyzed azide-alkyne cycloaddition (CuAAC) could offer medicinal chemistry scientists valuable data on designing and synthesizing novel triazole antifungals with more potent biological activities in their future research.

Design and Implementation of Digital Down Converter for WiFi Network

This paper introduces a field-programmable gate array (FPGA)-based digital down converter (DDC) processing a sampling frequency of about 3.64 GHz to a down-converted frequency of 28.4375 MHz to match the IEEE 802.11ah Wi-Fi HaLow standard. The proposed DDC uses a polyphase mixer(PM) and a resampling filter. The PM adopts parallel coordinate rotation digital computer (CORDIC) processors and lowpass filter arrays to reduce high-speed data rates with minimum resource utilization. Again, the resampling filter employs a cascaded integrator comb (CIC) filter associated with a parallel prefixed adder (PPA) and a multi-channel systolic finite impulse response (FIR) filter implemented with canonical expression, attaining optimum hardware cost. Converting floating-point to fixed-point data types provides significant resource savings. Finally, the improved design is coded in the Xilinx Vivado synthesis tool and successfully tested on the FPGA Kintex-7 device. In contrast to other recent architectures, the proposed design substantially reduces area requirements and power utilization. The Matlab tool verifies the design to achieve an acceptable spurious-free dynamic range (SFDR) of 115 dB.

A New Optimized Hybridization Approach for in silico High Throughput Molecular Docking on FPGA Platform.

The development process of a new drug should be a subject of continuous evolution and rapid improvement as drugs are essential to treat a wide range of diseases of which many are life-threatening. The advances in technology resulted in a novel track in drug discovery and development known as in silico drug design. The molecular docking phase plays a vital role in in silico drug development process. In this phase, thousands of 3D conformations of both the ligand and receptor are generated and the best conformations that create the most stable drug-receptor complex are determined. The speed in finding accurate and high-quality complexes depends on the efficiency of the search function in the molecular docking procedure. The objective of this research is to propose and implement a novel hybrid approach called hABCDE to replace the EMC searching part inside the BUDE docking algorithm. This helps in reaching the best solution in a much accelerated time and higher solution quality compared to using the ABC and DE algorithms separately. In this work, we have employed a new approach of hybridization between the Artificial Bee Colony (ABC) algorithm and the Differential Evolution (DE) algorithm as an alternative searching part of the Bristol University Docking Engine (BUDE) in order to accelerate the search for higher quality solutions. Moreover, the proposed docking approach was implemented on Field Programmable Gate Array (FPGA) parallel platform using Vivado High-Level Synthesis Tool (HLST) in order to optimize and enhance the execution time and overall efficiency. The NDM-1 protein was used as a model receptor in our experiments to demonstrate the efficiency of our approach. The NDM-1 protein was used as a model receptor in our experiments to demonstrate the efficiency of our approach. The results showed that the execution time for the BUDE with the new proposed hybridization approach was improved by 9,236 times. Our novel approach was significantly effective to improve the functionality of docking algorithms (Bristol University Docking Engine (BUDE)).

Area-Delay-Power Efficient VLSI Architecture 2D FIR Filter using Modified Multipliers and Adders

In this paper, a low power, area, and delay 2D Finite Impulse Response (FIR) filter architecture is derived from an analysis of a memory-efficient design. The completely direct-form 2D FIR filter is where the idea of parallel processing is first presented. As a result, the FIR filter may make better use of its memory by reusing its contents. With a block size of L and a filter length of N, a non-separable 2D FIR filter structure is developed and implemented. The FIR filter's arithmetic module makes use of high-speed, low-power multipliers and Carry Look Ahead (CLA) adders, with the output calculated by a pipelined adder unit. Verilog HDL code is used to represent the proposed architecture, and the CADENCE environment's NC Simulator and RTL Compiler synthesis tool are used to verify the design. Existing memory-efficient 2D FIR filter hardware architectures are compared to the produced area, power, and delay reports. Using Modified CLA (MCLA) adders and pipelining, we were able to cut down on power consumption by 44% and delay by 20%. Index Terms: Memory reuse, 2D-FIR filter, Low Power Multiplier, Parallel Prefix Adder, Carry Look Ahead adder, and pipelining.

Implementation of block-based diagonal and quadrantal symmetry type 2D-FIR filter architectures using DA technique

The efficient architectures of Two-Dimensional (2D) Finite Impulse Response (FIR) filters are proposed for image processing applications. The performance metrics such as power consumption, area, and delay of the architectures can be optimized using VLSI design. The 2D FIR filter architectures can be implemented using parallel or block processing to increase the filter throughput and to prune the number of clock cycles required for image processing. The symmetry in the filter coefficients decreases the number of multipliers, whereas the multiplier block is very complex and a power-consuming block in the filter architecture. In this work, two types of symmetric architectures such as diagonal symmetry and quadrantal symmetry filters are proposed. The remaining multipliers required for the filter architecture are replaced by Distributed Arithmetic (DA) logic. The memory-based DA reduces the LUT size and hence the area, power, and delay are reduced in the filter architecture. Block processing, symmetry, and DA concepts are introduced here to optimize the 2D FIR filter architecture. The proposed architectures are implemented in 45 nm CMOS technology using Cadence Genus Synthesis tools and area, delay, power, Area-Delay Product (ADP), and Power-Delay Product (PDP) results are obtained and compared with state-of-the-art works. The ADP value of the proposed diagonal symmetry architecture is decreased by a maximum of 73.34 %, and a minimum of 20.39 %, and the PDP value is decreased by a maximum of 90.28 %, and a minimum of 21.27 % when compared to the existing works. The ADP and PDP values of the proposed quadrantal symmetry are decreased by a minimum of 23.62 %, and 27.74 % when compared to existing works, respectively.

Hydroxyapatite: An Eco-Friendly Material for Enzyme Immobilization.

Biocatalysis has emerged in the last decade as a valuable and eco-friendly tool in chemical synthesis, allowing in several instances to reduce or eliminate the use of hazardous reagents, environmentally dangerous solvents and harsh reaction conditions. Enzymes are indeed able to catalyse chemical transformations on non-natural substrates under mild reaction conditions, still maintaining their high chemo-, regio-, and stereoselectivity. Enzyme immobilization, i. e. the grafting of enzymes on solid supports, can be viewed as an enabling technology, as it allows a better control of the reaction and the recycling of the biocatalyst, thus rendering economically viable the use of expensive enzymes also on a large scale. To pursue a sustainable approach, the supports for enzyme immobilization should be eco-friendly and possibly renewable. This review highlights the use of hydroxyapatite (HAP), an inorganic biomaterial able to confer strength and stiffness to the bone tissue in animals, as carrier for enzyme immobilization. HAP is a cheap, non-toxic and biocompatible material, with high surface area and protein affinity. Different enzyme classes, immobilization strategies, and the use of diverse HAP-based supports will be discussed, underlining the immobilization conditions and the properties of the obtained biocatalysts.

Adder-Only Reverse Converters for 5-Moduli Set {2 q , 2 q − 1, 2 q+1 ± 1, 2 q+2 − 1}

We propose a balanced 5 -moduli set P = { 2 q , 2 q − 1 , 2 q + 1 − 1 , 2 q + 1 + 1 , 2 q + 2 − 1 } , for odd q ≥ 3 . Our reverse conversion scheme is based on a multi-stage new Chinese remainder theorem, where some of the difficult-to-derive multiplicative inverse coefficients are expressed as geometric series. Only carry-save and ripple-carry adders are used as components of the corresponding circuits. Analytical gate-level evaluations are performed on the proposed and reference converters, whose results are confirmed by the synthesis tool. A comparison of the converters for moduli sets with equal dynamic range shows that P is similar to previous 5-moduli set { 2 q , 2 q − 1 , 2 q + 1 , 2 q + 1 − 1 , 2 q − 1 − 1 } and shows slight advantage in delay and energy consumption.

POSoligo software for in vitro gene synthesis

Oligonucleotide synthesis is vital for molecular experiments. Bioinformatics has been employed to create various algorithmic tools for the in vitro synthesis of nucleotides. The main approach to synthesizing long-chain DNA molecules involves linking short-chain oligonucleotides through ligase chain reaction (LCR) and polymerase chain reaction (PCR). Short-chain DNA molecules have low mutation rates, while LCR requires complementary interfaces at both ends of the two nucleic acid molecules or may alter the conformation of the nucleotide chain, leading to termination of amplification. Therefore, molecular melting temperature, length, and specificity must be considered during experimental design. POSoligo is a specialized offline tool for nucleotide fragment synthesis. It optimizes the oligonucleotide length and specificity based on input single-stranded DNA, producing multiple contiguous long strands (COS) and short patch strands (POS) with complementary ends. This process ensures free 5′- and 3′-ends during oligonucleotide synthesis, preventing secondary structure formation and ensuring specific binding between COS and POS without relying on stabilizing the complementary strands based on Tm values. POSoligo was used to synthesize the linear RBD sequence of SARS-CoV-2 using only one DNA strand, several POSs for LCR ligation, and two pairs of primers for PCR amplification in a time- and cost-effective manner.

Deep learning with plasma plume image sequences for anomaly detection and prediction of growth kinetics during pulsed laser deposition

Materials synthesis platforms that are designed for autonomous experimentation are capable of collecting multimodal diagnostic data that can be utilized for feedback to optimize material properties. Pulsed laser deposition (PLD) is emerging as a viable autonomous synthesis tool, and so the need arises to develop machine learning (ML) techniques that are capable of extracting information from in situ diagnostics. Here, we demonstrate that intensified-CCD image sequences of the plasma plume generated during PLD can be used for anomaly detection and the prediction of thin film growth kinetics. We develop multi-output (2 + 1)D convolutional neural network regression models that extract deep features from plume dynamics that not only correlate with the measured chamber pressure and incident laser energy, but more importantly, predict parameters of an auto-catalytic film growth model derived from in situ laser reflectivity experiments. Our results demonstrate how ML with in situ plume diagnostics data in PLD can be utilized to maintain deposition conditions in an optimal regime. Further, the predictive capabilities of plume dynamics on the kinetics of film growth or other film properties prior to deposition provides a means for rapid pre-screening of growth conditions for the non-expert, which promises to accelerate materials optimization with PLD.

Realizability modulo theories

In this paper we study the problem of realizability of reactive specifications written in LTLT, which is the extension of LTL where atomic propositions can be literals from a first-order theory, including arithmetic theories. We present a solution based on transforming LTLT specifications into purely Boolean specifications by (1) substituting theory literals by Boolean variables, and (2) computing an additional Boolean formula that captures the dependencies between the new variables imposed by the literals. We prove that the resulting specification is realizable if and only if the original specification is realizable. Moreover, the resulting specification can be passed to existing Boolean off-the-shelf synthesis and realizability tools, which can handle only Boolean LTL specifications.A second contribution is to prove that LTLT realizability of theories with a decidable ∃⁎∀⁎ fragment is decidable for all combinations of LTL temporal modalities. We present a simple version of our method, which relies on SMT solving, and performs a brute-force search to construct the “extra requirement”. A third contribution is an algorithm that checks whether a candidate is a correct Booleanization in non-Boolean LTL realizability.

Development of quality assessment tool for systematic reviews and meta-analyses of real-world studies: a Delphi consensus survey.

The increasing adoption of real-world studies in healthcare for decision making and planning has further necessitated the need for a specific quality assessment tool for evidence synthesis. This study aimed to develop a quality assessment tool for systematic reviews (SR) and meta-analysis (MA) involving real-world studies (QATSM-RWS) using a formal consensus method. Based on scoping review, the authors identified a list of items for possible inclusion in the quality assessment tool. A Delphi survey was formulated based on the identified items. A total of 89 experts, purposively recruited, with research experience in real-world data were invited to participate in the first round of Delphi survey. The participants who responded in the first Delphi round were invited to participate (n = 15) in the phrasing of the items. Strong level of agreement was found on the proposed list of items after the first round of Delphi. A rate of agreement ≥ 0.70 was used to define which items to keep in the tool. A list of 14 items emerged as suitable for QATSM-RWS. The items were structured under five domains: introduction, methods, results, discussions, and others. All participants agreed with the proposed phrasing of the items. This is the first study that has developed a specific tool that can be used to appraise the quality of SR and MA involving real-world studies. QATSM-RWS may be used by policymakers, clinicians, and practitioners when evaluating and generating real-world evidence. This tool is now undergoing validation process.

Zintl clusters as catalytic tools for synthesis

Although known for over a century, only now are Zintl clusters being investigated as catalysts. These clusters can serve as intermediaries between discrete molecules and bulk solids, allowing them to act as atom-precise models whose reactivity may map onto their heterogeneous counterparts. Key contributions in homogeneous Zintl catalysis are discussed.