This study focuses on the development and evaluation of a novel wound dressing material. l-arginine grafted poly(glycerol sebacate) materials (PGS-g-Arg) are developed by chemical conjugation of l-arginine on poly(glycerol sebacate) chains and the mechanical property, water vapor transmission rate, antimicrobial functions and biocompatibility are investigated. At various l-arginine grafting ratio, the mechanical properties are tunable. It was found that between 7–13% l-arginine grafting ratios, the tensile strengths of PGS-g-Arg were similar to that of natural skin. These materials are shown with a low water vapor transmission rate, 6.1 to 10.3 g/m2/h, which may form a barrier and assist in the closure of wounds. Inhibition in the growth of Pseudomonas aeruginosa and Staphylococcus aureus was observed on PGS-g-Arg, and a series of experiments were conducted to confirm its biocompatibility. In summary, l -arginine grafted poly(glycerol sebacate) may offer a novel option for wound dressing.
Digital light processing additive manufacturing (DLP-AM) technology has received a lot of attention in the field of biomedical engineering due to its high precision and customizability. However, some photoinitiators, as one of the key components in DLP-AM, may present toxicity and limit the application of DLP-AM toward biomedical applications. In order to gain further insights into the correlation between biocompatibility and photoinitiators in photoresins, a study on the selection of photoinitiators used in DLP-AM is conducted. The light absorbance range and cytocompatibility of four photoinitiators, vitamin B2 combined with triethanolamine (B2/TEOA), diphenyl(2,4,6-trimethylbenzoyl)phosphine oxide (TPO), 2-dimethoxy-2-phenylacetophenone (DMPA), and 2-hydroxy-4-(2-hydroxyethoxy)-2-methylpropiophenone (I2959), are characterized. Each photoinitiator is then combined with poly(glycerol sebacate) acrylate (PGSA) and poly(ε-caprolactone) diacrylate (PCLDA), to evaluate their miscibility and film formation ability through photopolymerization. The mechanical properties, in vitro and in vivo biocompatibility studies on bulk films are investigated. It is found that B2/TEOA and TPO exhibit a wider light absorbance range than I2959 and DMPA. PGSA films with B2/TEOA (PGSA-B2/TEOA) is capable of sustaining cell proliferation up to 10 days and showing low immune responses after 14 days post implantation, proving its biocompatibility. Although B2/TEOA requires longer photopolymerization time, the mechanical strength of PGSA-B2/TEOA is comparable to PGSA films with TPO and DMPA, and this combination is 3D-printable through DLP-AM at the rate of 100 s per layer. In summary, B2/TEOA is a promising photoinitiator for 3D printing.
Among several emerging architectures, computing in memory (CIM), which features in-situ analog computation , is a potential solution to the data movement bottleneck of the Von Neumann architecture for artificial intelligence (AI). Interestingly, more strengths of CIM significantly different from in-situ analog computation are not widely known yet. In this work, we point out that mutually stationary vectors (MSVs) , which can be maximized by introducing associativity to CIM, are another inherent power unique to CIM. By MSVs, CIM exhibits significant freedom to dynamically vectorize the stored data (e.g., weights) to perform agile computation using the dynamically formed vectors. We have designed and realized an SA-CIM silicon prototype and corresponding architecture and acceleration schemes in the TSMC 28 nm process. More specifically, the contributions of this paper are fivefold: 1) We identify MSVs as new features that can be exploited to improve the current performance and energy challenges of the CIM-based hardware. 2) We propose SA-CIM to enhance MSVs (input-reordering flexibility) for skipping the zeros, small values, and sparse vectors. 3) We propose channel swapping to enhance the zero-skipping technique. 4) We propose a transposed systolic dataflow to efficiently conduct conv3×3 while being capable of exploiting input-skipping schemes. 5) We propose a design flow to search for optimal aggressive skipping scheme setups while satisfying the accuracy loss constraint. The proposed ISSA architecture improves the throughput by 1.91× to 2.97× speedup and the energy efficiency by 2.5× to 4.2×.
Compute-in-memory (CIM) architecture is promising for its in-situ analog computing ability. However, one practical constraint for CIM architectures is the limited number of activated rows in an operation Unit (OU). OU-based CIM architecture only activates a subgroup of memory cells to ensure a large signal margin and enough consideration of non-ideal device/circuit effect, which pays the cost of lowered computing throughput. In short, the OU-based CIM architectures suffer from array underutilization to ensure high accuracy.This work proposes a novel architecture, BICEP, which exploits bitline inversion technique to enlarge the OU size without the need to prune, approximate, and retrain. More specifically, the key contributions of this work are threefold: 1) We propose a bitline inversion scheme, which guarantees more than 2× larger OU size without affecting the numerical results and the ADC resolution. The key insight is to selectively apply code inversion on heavy bitlines to constrain their MAC outputs and compensate using low-cost compensation units. We mathematically prove that the proposal can be applied to both single- and multi-level cells (SLC and MLC). 2) We propose an inversion-aware weight swapping scheme, which swaps the weight order to maximize the OU size exploiting bitline inversion. 3) We propose weight order propagation to enable inversion-aware weight swapping without storage overheads. The extensive experiments on ImageNet classification tasks demonstrate that this work outperforms state-of-the-art OU-based CIM architecture (DL-RSIM) by up to 2.06× speedup and 1.97× energy efficiency.
Among several emerging architectures, computing in memory (CIM), which features in-situ analog computation, is a potential solution to the data movement bottleneck of the Von Neumann architecture for artificial intelligence (AI). Interestingly, more strengths of CIM significantly different from in-situ analog computation are not widely known yet. In this work, we point out that mutually stationary vectors (MSVs), which can be maximized by introducing associativity to CIM, are another inherent power unique to CIM. By MSVs, CIM exhibits significant freedom to dynamically vectorize the stored data (e.g., weights) to perform agile computation using the dynamically formed vectors.
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