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With the advancement in CMOS technology and multiple processors on the chip, communication across these cores is managed by a network-on-chip (NoC). Power and performance of these NoC interconnects have become a significant factor.The authors aim to reduce the leakage power consumption of NoC buffers by the use of non-volatile spin transfer torque random access memory (STT-RAM)-based buffers. STT-RAM technology has the advantages of high density and low leakage but suffers from low endurance. This low endurance has an impact on the lifetime of the router on the whole due to unwanted write-variations governed by virtual channel (VC) allocation policies. Here various VC allocation policies that help the uniform distribution of the writes across the buffers are proposed. Iso-capacity and iso-area-based alternatives to replace SRAM buffers with STT-RAM buffers are also presented. Pure STT-RAM buffers, however, impact the network latency. To mitigate this, a hybrid variant of the proposed policies which uses alternative VCs made of SRAM technology in the case of heavy network traffic is proposed. Experimental evaluation of full system simulation shows that proposed policies reduce the write variation by 99% and improve lifetime by 3.2 times and 1093 times, respectively. Also a 55.5% gain in the energy delay product is obtained.

In this paper, a comparison has been drawn between 5 transistor (5T), 6T and 7T SRAM cells. All the cells have been designed using both single-threshold (conventional) and dual-threshold (dual-Vt) voltage techniques. Their respective delays and power consumption have been calculated at 180 nm and 65 nm CMOS technology. With technology scaling, power consumption decreases by 80% to 90%, with some increase in write time because of the utilization of high- Vt transistors in write critical path. The results show that the read delay of 7T SRAM cell is 9% lesser than 5T SRAM cell and 29% lesser than 6T SRAM cell due to the lower resistance of the read access delay path. While read power of 5T SRAM cell is reduced by 10% and 24% as compared to 7T SRAM, 6T SRAM cell respectively. The write speed, however, is degraded by 1% to 3% with the 7T and 5T SRAM cells as compared to the 6T SRAM cells due to the utilization of single ended architecture. While write power of 5T SRAM cell is reduced by up to 40% and 67% as compared to 7T SRAM, 6T SRAM cell respectively.

Power consumption and data processing speed of integrated circuits (ICs) is an increasing concern in many emerging Artificial Intelligence (AI) applications, such as autonomous vehicles and Internet of Things (IoT). Existing state-of-the-art SRAM architectures for AI computing are highly accurate and can provide high throughput. However, these SRAMs have problems that they consume high power and occupy a large area to accommodate complex AI models. A carbon nanotube field-effect transistors (CNFET) device has been reported as a potential candidates for AI devices requiring ultra-low power and high-throughput due to their satisfactory carrier mobility and symmetrical, good subthreshold electrical performance. Based on the CNFET and FinFET device’s electrical performance, we propose novel ultra-low power and high-throughput 8T SRAMs to circumvent the power and the throughput issues in Artificial Intelligent (AI) computation for autonomous vehicles. We propose two types of novel 8T SRAMs, P-Latch N-Access (PLNA) 8T SRAM structure and single-ended (SE) 8T SRAM structure, and compare the performance with existing state-of-the-art 8T SRAM architectures in terms of power consumption and speed. In the SRAM circuits of the FinFET and CNFET, higher tube and fin numbers lead to higher operating speed. However, the large number of tubes and fins can lead to larger area and more power consumption. Therefore, we optimize the area by reducing the number of tubes and fins without compromising the memory circuit speed and power. Most importantly, the decoupled reading and writing of our new SRAMs cell offers better low-power operation due to the stacking of device in the reading part, as well as achieving better readability and writability, while offering read Static Noise Margin (SNM) free because of isolated reading path, writing path, and greater pull up ratio. In addition, the proposed 8T SRAMs show even better performance in delay and power when we combine them with the collaborated voltage sense amplifier and independent read component. The proposed PLNA 8T SRAM can save 96%, while the proposed SE 8T SRAM saves around 99% in writing power consumption compared with the existing state-of-the-art 8T SRAM in FinFET model, as well as 99% for writing operation in CNFET model.

Low-power and secure crypto-devices are in crucial demand for the current emerging technology of the Internet of Things (IoT). In nanometer CMOS technology, the static and dynamic power consumptions are in a very critical challenge. Therefore, the FinFETs is an alternative technology due to its superior attributes of non-leakage power, intra-die variability, low-voltage operation, and lower retention voltage of SRAMs. In this study, our previous work on CMOS two-phase clocking adiabatic physical unclonable function (TPCA-PUF) is evaluated in a FinFET device with a 4-bits PUF circuit complexity. The TPCA-PUF-based shorted-gate (SG) and independent-gate (IG) modes of FinFETs are investigated under various ambient temperatures, process variations, and ±20% of supply voltage variations. To validate the proposed TPCA-PUF circuit, the QUALPFU-based Fin-FETs are compared in terms of cyclical energy dissipation, the security metrics of the uniqueness, the reliability, and the bit-error-rate (BER). The proposed TPCA-PUF is simulated using 45 nm process technology with a supply voltage of 1 V. The uniqueness, reliability, and the BER of the proposed TPCA-PUF are 50.13%, 99.57%, and 0.43%, respectively. In addition, it requires a start-up power of 18.32 nW and consumes energy of 2.3 fJ/bit/cycle at the reference temperature of 27 °C.

PurposeBy extending the service robot acceptance model (sRAM), this study aims to explore and enhance the acceptance of chatbots. The study considered functional, relational, social, user and gratification elements in determining the acceptance of chatbots.Design/methodology/approachBy using the purposive sampling technique, data of 321 service customers, gathered from millennials through a questionnaire and subsequent PLS-SEM modeling, was applied for hypotheses testing.FindingsFindings revealed that the functional elements, perceived usefulness and perceived ease of use affect acceptance of chatbots. However, in social elements, only perceived social interactivity affects the acceptance of chatbots. Moreover, both user and gratification elements (hedonic motivation and symbolic motivation) significantly influence the acceptance of chatbots. Lastly, trust is the only contributing factor for the acceptance of chatbots in the relational elements.Practical implicationsThe study extends the literature related to chatbots and offers several guidelines to the service industry to effectively employ chatbots.Originality/valueThis is one of the first studies that used newly developed sRAM in determining chatbot acceptance. Moreover, the study extended the sRAM by adding user and gratification elements and privacy concerns as originally sRAM model was limited to functional, relational and social elements.

This study aims for a new 11T static random access memory (SRAM) cell that uses power gating transistors and transmission gate for low leakage and reliable write operation. The proposed cell has a separate read and write path which successfully improves read and write abilities. Furthermore, it solves the row half select disturbance and utilises a row-based virtual ground signal to eliminate unnecessary bit-line discharge in the un-selected row, thus decreasing energy consumption. The cell also achieves low power due to the stack effect. To show the effectiveness of the cell, its design metrics are compared with other published SRAM cells, namely, conventional 6T, 10T, 9T, and power-gated 9T (PG9T). In standby mode, from 6.71 to 7.37% leakage power reduction is observed for this cell at an operating voltage of 1.2 V and 29.21 to 58.68% & 32.74 to 71.11% improvement for write & read power over other cells. The proposed cell exhibits higher write and reads static noise margins with an improvement of 13.54 and 63.28%, respectively, compared to conventional 6T SRAM cell. The cell provides write delay improvement from 29.77 to 49.40% and read delay improvement from 7 to 12% compared to 9T, 10T, and PG9T, respectively.

The SRAM cells are used in many biomedical applications such as Pace makers,ECG devices,body area networks etc.,where power consumption will be the main constraint. The conventional 6T SRAM cell has reduced stability and more power consumption when technology is scaled resulting in supply voltage scaling, so other alternative SRAM cells from 7T to 12T have been proposed which can address these problems. Here a low power 7T SRAM cell is suggested which has low power consumption and condensed leakage currents and power dissipation. The projected design has a leakage power of 5.31nW and leakage current of 7.58nA which is 84.9 % less than the 7T SRAM cell without using the proposed leakage reduction technique and it is 22.4 % better than 6T SRAM and 22.1 % better than 8T SRAM cell when both use the same leakage reduction technique. The cell area of the 7T SRAM cell is 1.25µm 2 , 6T SRAM is 1.079µm 2 and that of 8T SRAM is 1.28µm 2 all the results are simulated in cadence virtuoso using 18nm technology.

Reformer reduces redundant self‐attention computations via hash bucketing. In this study, we introduce a SRAM‐based digital‐analogue hybrid reformer computing‐in‐memory (ReCIM) accelerator macro. This macro presents an absolute maximum value addressing circuit which facilitates the hash bucketing process and enables the utilisation of strongly‐correlated (S‐C) vectors for attention mechanism computations, thereby improving computational efficiency and saving memory space. Additionally, we introduce a reusable weight array which is suitable for matrix operations across various processes of self‐attention, minimising unnecessary area overhead and enhancing device reusability. The proposed 4 Kb ReCIM macro was analysed using 28‐nm CMOS technology. Simulation results demonstrate that the macro achieves a frequency of 500 MHz at a supply voltage of 0.9 V. During the hash bucketing process, energy efficiency reaches 9.74 TOPS/W. In this study, we introduce a SRAM‐based digital‐analogue hybrid reformer computing‐in‐memory accelerator macro. This macro presents an absolute maximum value addressing circuit which facilitates the hash bucketing process and enables the utilisation of strongly‐correlated (S‐C) vectors for attention mechanism computations, thereby improving computational efficiency and saving memory space. Simulation results show that the data processing frequency for implementing hash bucketing is as high as 500 MHz, and the energy efficiency is 9.74 TOPS/W.

Artificial intelligence (AI) has revolutionized present-day life through automation and independent decision-making capabilities. For AI hardware implementations, the 6T-SRAM cell is a suitable candidate due to its performance edge over its counterparts. However, modern AI hardware such as neural networks (NNs) access off-chip data quite often, degrading the overall system performance. Compute-in-memory (CIM) reduces off-chip data access transactions. One CIM approach is based on the mixed-signal domain, but it suffers from limited bit precision and signal margin issues. An alternate emerging approach uses the all-digital signal domain that provides better signal margins and bit precision; however, it will be at the expense of hardware overhead. We have analyzed digital signal domain CIM silicon-verified 6T-SRAM CIM solutions, after classifying them as SRAM-based accelerators, i.e., near-memory computing (NMC), and custom SRAM-based CIM, i.e., in-memory-computing (IMC). We have focused on multiply and accumulate (MAC) as the most frequent operation in convolution neural networks (CNNs) and compared state-of-the-art implementations. Neural networks with low weight precision, i.e., <12b, show lower accuracy but higher power efficiency. An input precision of 8b achieves implementation requirements. The maximum performance reported is 7.49 TOPS at 330 MHz, while custom SRAM-based performance has shown a maximum of 5.6 GOPS at 100 MHz. The second part of this article analyzes the FinFET 6T-SRAM as one of the critical components in determining overall performance of an AI computing system. We have investigated the FinFET 6T-SRAM cell performance and limitations as dictated by the FinFET technology-specific parameters, such as sizing, threshold voltage (Vth), supply voltage (VDD), and process and environmental variations. The HD FinFET 6T-SRAM cell shows 32% lower read access time and 1.09 times better leakage power as compared with the HC cell configuration. The minimum achievable supply voltage is 600 mV without utilization of any read- or write-assist scheme for all cell configurations, while temperature variations show noise margin deviation of up to 22% of the nominal values.

In this paper, a single-ended, dual port, 1R1 W seven transistor-based static random access memory bit cell is presented. The cell is designed based on a detailed review of various pre-existing 7T cells. All the cells in the paper are evaluated at 32 nm technology and supply voltage of 0.8 V. The static analysis reveals that the hold/read noise margins for the proposed cell are 324 mV each, whereas the write margin is 488 mV. Successful read and write operation for the cell requires a pulse-width of 5 ps and 0.14 ns, respectively. The temperature variation and process corner analysis are used to justify the reliability for the proposed cell. The former analysis yields 0.15 and 0.24 mV/°C variation in the HSNM/RSNM, and WM for the cell. The current ratio for the cell is comparatively higher than other cells. The leakage power consumption for the cell is 256 pW, while its read and write power consumption are 6 μW and 1.9 μW, respectively. Also, the cell is half select disturbance-free and therefore supports bit-interleaving. This ensures the increased reliability for the cell. All the aforementioned merits for the cells are achieved with a minimal layout area of 0.553 μm².