相关论文: Generalized Water-filling for Source-aware Energy-…
We consider a wireless network where multiple energy harvesting transmitters communicate with the common receiver in a time sharing manner. In each slot, a transmitter can either harvest energy or send its data to the receiver. Given a time…
Prior studies have shown that the retention time of the non-volatile spin-transfer torque RAM (STT-RAM) can be relaxed in order to reduce STT-RAM's write energy and latency. However, since different applications may require different…
Resource efficiency plays an important role for machine learning nowadays. The energy and decision latency are two critical aspects to ensure a sustainable and practical application. Unfortunately, the energy consumption and decision…
Non-volatile memory, such as resistive RAM (RRAM), is an emerging energy-efficient storage, especially for low-power machine learning models on the edge. It is reported, however, that the bit error rate of RRAMs can be up to 3.3% in the…
Point clouds are increasingly important in intelligent applications, but frequent off-chip memory traffic in accelerators causes pipeline stalls and leads to high energy consumption. While conventional line buffer techniques can eliminate…
Water-filling solutions play an important role in the designs for wireless communications, e.g., transmit covariance matrix design. A traditional physical understanding is to use the analogy of pouring water over a pool with fluctuating…
Self-supervised learning (SSL) has advanced speech processing but suffers from quadratic complexity due to self-attention. To address this, SummaryMixing (SM) has been proposed as a linear-time alternative that summarizes entire utterances…
We explore how to improve the energy performance of battery-less Internet of Things (IoT) devices at the cost of a reduction in the quality of the output. Battery-less IoT devices are extremely resource-constrained energy-harvesting…
For Industrial Wireless Sensor Networks, it is essential to reliably sense and deliver the environmental data on time to avoid system malfunction. While energy harvesting is a promising technique to extend the lifetime of sensor nodes, it…
Deep neural network (DNN) accelerators received considerable attention in past years due to saved energy compared to mainstream hardware. Low-voltage operation of DNN accelerators allows to further reduce energy consumption significantly,…
Recent development in memory technologies has introduced Spin-Transfer Torque Magnetic RAM (STT-MRAM) as the most promising replacement for SRAMs in on-chip cache memories. Besides its lower leakage power, higher density, immunity to…
Compute-in-memory (CiM) is a promising approach to improving the computing speed and energy efficiency in dataintensive applications. Beyond existing CiM techniques of bitwise logic-in-memory operations and dot product operations, this…
Deeply embedded systems often have the tightest constraints on energy consumption, requiring that they consume tiny amounts of current and run on batteries for years. However, they typically execute code directly from flash, instead of the…
Spin transfer torque magnetic random access memory (STT-MRAM) is considered as one of the most promising candidates to build up a true universal memory thanks to its fast write/read speed, infinite endurance and non-volatility. However the…
This paper considers the maximization of information rates for the Gaussian frequency-selective interference channel, subject to power and spectral mask constraints on each link. To derive decentralized solutions that do not require any…
In this work, we propose the design of modulation schemes that improve the rate-energy region of fluid antenna-assisted simultaneous wireless information and power transfer (SWIPT) systems. By considering the nonlinear characteristics of…
Content addressable memory is popular in intelligent computing systems as it allows parallel content-searching in memory. Emerging CAMs show a promising increase in bitcell density and a decrease in power consumption than pure CMOS…
Many convolutional neural network (CNN) accelerators face performance- and energy-efficiency challenges which are crucial for embedded implementations, due to high DRAM access latency and energy. Recently, some DRAM architectures have been…
We reduce the memory footprint of popular large-scale online learning methods by projecting our weight vector onto a coarse discrete set using randomized rounding. Compared to standard 32-bit float encodings, this reduces RAM usage by more…
With computers getting more and more powerful and integrated in our daily lives, the focus is increasingly shifting towards more human-friendly interfaces, making Automatic Speech Recognition (ASR) a central player as the ideal means of…