Energy Efficient Error Resilient Multiplier Using Low-power Compressors

The approximate hardware design can save huge energy at the cost of errors incurred in the design. This article proposes the approximate algorithm for low-power compressors, utilized to build approximate multiplier with low energy and acceptable error profiles. This article presents two design approaches (DA1 and DA2) for higher bit size approximate multipliers. The proposed multiplier of DA1 have no propagation of carry signal from LSB to MSB, resulted in a very high-speed design. The increment in delay, power, and energy are not exponential with increment of multiplier size ( n ) for DA1 multiplier. It can be observed that the maximum combinations lie in the threshold Error Distance of 5% of the maximum value possible for any particular multiplier of size n . The proposed 4-bit DA1 multiplier consumes only 1.3 fJ of energy, which is 87.9%, 78%, 94%, 67.5%, and 58.9% less when compared to M1, M2, LxA, MxA, accurate designs respectively. The DA2 approach is recursive method, i.e., n -bit multiplier built with n/2-bit sub-multipliers. The proposed 8-bit multiplication has 92% energy savings with Mean Relative Error Distance (MRED) of 0.3 for the DA1 approach and at least 11% to 40% of energy savings with MRED of 0.08 for the DA2 approach. The proposed multipliers are employed in the image processing algorithm of DCT, and the quality is evaluated. The standard PSNR metric is 55 dB for less approximation and 35 dB for maximum approximation.

Double-Shift: A Low-Power DNN Weights Storage and Access Framework based on Approximate Decomposition and Quantization

One major challenge in deploying Deep Neural Network (DNN) in resource-constrained applications, such as edge nodes, mobile embedded systems, and IoT devices, is its high energy cost. The emerging approximate computing methodology can effectively reduce the energy consumption during the computing process in DNN. However, a recent study shows that the weight storage and access operations can dominate DNN's energy consumption due to the fact that the huge size of DNN weights must be stored in the high-energy-cost DRAM. In this paper, we propose Double-Shift, a low-power DNN weight storage and access framework, to solve this problem. Enabled by approximate decomposition and quantization, Double-Shift can reduce the data size of the weights effectively. By designing a novel weight storage allocation strategy, Double-Shift can boost the energy efficiency by trading the energy consuming weight storage and access operations for low-energy-cost computations. Our experimental results show that Double-Shift can reduce DNN weights to 3.96%–6.38% of the original size and achieve an energy saving of 86.47%–93.62%, while introducing a DNN classification error within 2%.

Enabling Cross-technology Communication from LoRa to ZigBee via Payload Encoding in Sub-1 GHz Bands

Low-power wireless mesh networks (LPWMNs) have been widely used in wireless monitoring and control applications. Although LPWMNs work satisfactorily most of the time thanks to decades of research, they are often complex, inelastic to change, and difficult to manage once the networks are deployed. Moreover, the deliveries of control commands, especially those carrying urgent information such as emergency alarms, suffer long delay, since the messages must go through the hop-by-hop transport. Recent studies show that adding low-power wide-area network radios such as LoRa onto the LPWMN devices (e.g., ZigBee) effectively overcomes the limitation. However, users have shown a marked reluctance to embrace the new heterogeneous communication approach because of the cost of hardware modification. In this article, we introduce LoRaBee, a novel LoRa to ZigBee cross-technology communication (CTC) approach, which leverages the energy emission in the Sub-1 GHz bands as the carrier to deliver information. Although LoRa and ZigBee adopt distinct modulation techniques, LoRaBee sends information from LoRa to ZigBee by putting specific bytes in the payload of legitimate LoRa packets. The bytes are selected such that the corresponding LoRa chirps can be recognized by the ZigBee devices through sampling the received signal strength. Experimental results show that our LoRaBee provides reliable CTC communication from LoRa to ZigBee with the throughput of up to 281.61 bps in the Sub-1 GHz bands.

Attack-aware Synchronization-free Data Timestamping in LoRaWAN

Low-power wide-area network technologies such as long-range wide-area network (LoRaWAN) are promising for collecting low-rate monitoring data from geographically distributed sensors, in which timestamping the sensor data is a critical system function. This article considers a synchronization-free approach to timestamping LoRaWAN uplink data based on signal arrival time at the gateway, which well matches LoRaWAN’s one-hop star topology and releases bandwidth from transmitting timestamps and synchronizing end devices’ clocks at all times. However, we show that this approach is susceptible to a frame delay attack consisting of malicious frame collision and delayed replay. Real experiments show that the attack can affect the end devices in large areas up to about 50,000, m 2 . In a broader sense, the attack threatens any system functions requiring timely deliveries of LoRaWAN frames. To address this threat, we propose a LoRaTS gateway design that integrates a commodity LoRaWAN gateway and a low-power software-defined radio receiver to track the inherent frequency biases of the end devices. Based on an analytic model of LoRa’s chirp spread spectrum modulation, we develop signal processing algorithms to estimate the frequency biases with high accuracy beyond that achieved by LoRa’s default demodulation. The accurate frequency bias tracking capability enables the detection of the attack that introduces additional frequency biases. We also investigate and implement a more crafty attack that uses advanced radio apparatuses to eliminate the frequency biases. To address this crafty attack, we propose a pseudorandom interval hopping scheme to enhance our frequency bias tracking approach. Extensive experiments show the effectiveness of our approach in deployments with real affecting factors such as temperature variations.

How large entrepreneurial-oriented companies breed innovation: the roles of interdepartmental collaboration and organizational culture

Purpose This study aims to provide a better understanding into how large-scale companies overcome their rigidity and bureaucracy, and transform entrepreneurial orientation (EO) into organizational responsiveness (e.g. interdepartmental collaboration [IDC]). It also clarifies the double-edged role of organizational culture in shaping IDC, specifically within the Chinese economy that is deeply influenced by Confucianism. Design/methodology/approach Datas were randomly collected from companies that reside in the Yangtze River Delta region. With a sample of 115 large-scale EO Chinese firms, consisting of 402 department managers and 115 executives. The study aggregates the scores to create an overall measurement for EO, collectivism, power distance and IDC in the analysis. Further, confirmatory factor analyses were used to measure the structural model fitness, and multiple regression analysis was used to assess the hypotheses. Findings The results show that in competitive environments, IDC, as a strategic response to EO and a risk aversion of inertia and bureaucracy, fully mediates the positive relationship between EO and organizational creativity. Furthermore, the positive association is more pronounced under high cultural collectivism or low power distance in large-scale firms. Research limitations/implications This paper contributes to the understanding of EO approach at the organizational level. The results posit that when large companies adopt EO, they are proactive rather than passive and would exhibit IDC as an important strategic responsiveness. Moreover, different organizational cultural orientations (i.e. high collectivism and low power distance) help to build IDC before cultivating innovation. Practical implications The results in this study suggest that large companies should focus on developing IDC to overcome knowledge fragmentation, bureaucracy and inertia. Also, large firms should develop Human Resource Management practices, such as creating job rotation and workflow, as well as cultural trust and common beliefs. In contrast, they should be on guard against status differences and workplace hierarchy’s cultural context. Originality/value To the best of the authors’ knowledge, this is the first study that considers the roles of IDC and organizational culture and examines how large-scale entrepreneurial-oriented companies breed innovation.