Computing-in-memory(CIM)offers a promising solution to the memory wall issue.Magnetoresistive random-access memory(MRAM)is a favored medium for CIM due to its non-volatility,high speed,low power,and technology maturit...Computing-in-memory(CIM)offers a promising solution to the memory wall issue.Magnetoresistive random-access memory(MRAM)is a favored medium for CIM due to its non-volatility,high speed,low power,and technology maturity.However,MRAM has continuously encountered the challenge of an insufficient high-resistance state(HRS)to low-resistance state(LRS)ratio,which affects the result accuracy of CIM.In this paper,based on SOT devices,we propose a 5T2M bit-cell structure that increases the high-to-low current ratio by modulating the sub-threshold operation region.Besides,by jointly using high-resistance devices(MΩ level),the power consumption of the bit-cell array can be significantly reduced.Simultaneously,we have designed a compatible multi-bit implementation and macro architecture to support AI edge inference acceleration.This work was simulated under a 40-nm foundry process and a physically verified SOT-MTJ model.The results show that under the same high-to-low resistance ratio,a 52.6×high-to-low current ratio can be achieved,along with a 38.6%-98%bit-cell array power reduction.展开更多
针对无线和电力线通信混合组网的信道竞争接入问题,提出了一种基于深度强化学习的电力线与无线双模通信的MAC接入算法。双模节点根据网络广播信息和信道使用等数据自适应接入双媒质信道。首先建立了基于双模通信网络交互和统计信息的双...针对无线和电力线通信混合组网的信道竞争接入问题,提出了一种基于深度强化学习的电力线与无线双模通信的MAC接入算法。双模节点根据网络广播信息和信道使用等数据自适应接入双媒质信道。首先建立了基于双模通信网络交互和统计信息的双模通信节点数据采集模型;接着定义了基于协作信息的深度强化学习(deep reinforcement learning,DRL)状态空间、动作空间和奖励,设计了联合α-公平效用函数和P坚持接入机制的节点决策流程,实现基于双深度Q网络(double deep Q-network,DDQN)的双模节点自适应接入算法;最后进行算法性能仿真和对比分析。仿真结果表明,提出的接入算法能够在保证双模网络和信道接入公平性的条件下,有效提高双模通信节点的接入性能。展开更多
低功耗广域网(Low Power Wide Area Network,LPWAN)技术的出现,能够在保证更远距离的通信传输的同时,最大限度地降低功耗,节约传输成本。LoRa(Long Range)技术作为其中的佼佼者,凭借其远距离、低功耗、大容量、强抗干扰、高接收灵敏度...低功耗广域网(Low Power Wide Area Network,LPWAN)技术的出现,能够在保证更远距离的通信传输的同时,最大限度地降低功耗,节约传输成本。LoRa(Long Range)技术作为其中的佼佼者,凭借其远距离、低功耗、大容量、强抗干扰、高接收灵敏度的特点,备受工业界和学术界的青睐。针对目前工业中主流使用的基于ALOHA的LoRaWAN协议无法很好地解决海量终端设备接入LoRa网络后所带来的严重数据包冲突以及LoRa CAD(Channel Activity Detection)功能带来的隐藏终端问题,提出了一种基于BTMA(Busy Tone Multiple Access)的LoRa网络MAC协议——BT-MAC协议。该协议利用了LoRa高接收灵敏度的特性,网关利用“忙音”信标来通知各个节点网关的工作情况,减少了无效包的发送。同时,节点端通过记录有“忙音”信息和本地信息的逻辑信道矩阵,结合最优信道选择算法,选出最优逻辑信道进行发送,降低了端节点上行数据包之间的冲突,有效缓解了LoRa网络中的隐藏终端问题以及阻塞问题。此外,搭建了LoRa网络MAC协议测试平台,并测试了BT-MAC的有效性,完成了室内和室外环境大规模的并发实验和能耗检测实验。实验结果表明,BT-MAC协议的吞吐量是LMAC-2协议的1.6倍,是ALOHA协议的5.1倍;同时其包接收率达到LMAC-2协议的1.53倍,ALOHA协议的17.2倍;其包接收平均能耗约为LMAC-2协议的64.1%,为ALOHA协议的14.2%。展开更多
针对PTP(precise time protocol)协议在应用层获取软件时间戳导致时钟同步精度下降的问题,提出一种基于MAC(media access control)层获取硬件时间戳的PTP同步优化方案。设计了以STM32F407微处理器为核心的PTP时钟应用平台,在MAC层实现...针对PTP(precise time protocol)协议在应用层获取软件时间戳导致时钟同步精度下降的问题,提出一种基于MAC(media access control)层获取硬件时间戳的PTP同步优化方案。设计了以STM32F407微处理器为核心的PTP时钟应用平台,在MAC层实现了硬件时间戳获取,避免了由于协议栈软件处理延时产生的不确定性;针对PTP时钟晶振老化导致的时间同步偏差及网络延迟抖动问题,采用迭代方法优化了本地时钟频率调节算法,提高了频率校正精度。经实际测试,主从时钟偏差的RMS(root mean square)优于20 ns,提升了时钟同步精度。展开更多
基金supported in part by the Beijing MSTC Program under Grant Z231100007423019in part by Beijing Natural Science Foundation under Grant L223004+1 种基金in part by the Natural Science Foundation of China under Grant 62274008in part by the Research Funding of Hangzhou International Innovation Institute of Beihang University under Grant 2024KQ157。
文摘Computing-in-memory(CIM)offers a promising solution to the memory wall issue.Magnetoresistive random-access memory(MRAM)is a favored medium for CIM due to its non-volatility,high speed,low power,and technology maturity.However,MRAM has continuously encountered the challenge of an insufficient high-resistance state(HRS)to low-resistance state(LRS)ratio,which affects the result accuracy of CIM.In this paper,based on SOT devices,we propose a 5T2M bit-cell structure that increases the high-to-low current ratio by modulating the sub-threshold operation region.Besides,by jointly using high-resistance devices(MΩ level),the power consumption of the bit-cell array can be significantly reduced.Simultaneously,we have designed a compatible multi-bit implementation and macro architecture to support AI edge inference acceleration.This work was simulated under a 40-nm foundry process and a physically verified SOT-MTJ model.The results show that under the same high-to-low resistance ratio,a 52.6×high-to-low current ratio can be achieved,along with a 38.6%-98%bit-cell array power reduction.
文摘针对无线和电力线通信混合组网的信道竞争接入问题,提出了一种基于深度强化学习的电力线与无线双模通信的MAC接入算法。双模节点根据网络广播信息和信道使用等数据自适应接入双媒质信道。首先建立了基于双模通信网络交互和统计信息的双模通信节点数据采集模型;接着定义了基于协作信息的深度强化学习(deep reinforcement learning,DRL)状态空间、动作空间和奖励,设计了联合α-公平效用函数和P坚持接入机制的节点决策流程,实现基于双深度Q网络(double deep Q-network,DDQN)的双模节点自适应接入算法;最后进行算法性能仿真和对比分析。仿真结果表明,提出的接入算法能够在保证双模网络和信道接入公平性的条件下,有效提高双模通信节点的接入性能。
文摘低功耗广域网(Low Power Wide Area Network,LPWAN)技术的出现,能够在保证更远距离的通信传输的同时,最大限度地降低功耗,节约传输成本。LoRa(Long Range)技术作为其中的佼佼者,凭借其远距离、低功耗、大容量、强抗干扰、高接收灵敏度的特点,备受工业界和学术界的青睐。针对目前工业中主流使用的基于ALOHA的LoRaWAN协议无法很好地解决海量终端设备接入LoRa网络后所带来的严重数据包冲突以及LoRa CAD(Channel Activity Detection)功能带来的隐藏终端问题,提出了一种基于BTMA(Busy Tone Multiple Access)的LoRa网络MAC协议——BT-MAC协议。该协议利用了LoRa高接收灵敏度的特性,网关利用“忙音”信标来通知各个节点网关的工作情况,减少了无效包的发送。同时,节点端通过记录有“忙音”信息和本地信息的逻辑信道矩阵,结合最优信道选择算法,选出最优逻辑信道进行发送,降低了端节点上行数据包之间的冲突,有效缓解了LoRa网络中的隐藏终端问题以及阻塞问题。此外,搭建了LoRa网络MAC协议测试平台,并测试了BT-MAC的有效性,完成了室内和室外环境大规模的并发实验和能耗检测实验。实验结果表明,BT-MAC协议的吞吐量是LMAC-2协议的1.6倍,是ALOHA协议的5.1倍;同时其包接收率达到LMAC-2协议的1.53倍,ALOHA协议的17.2倍;其包接收平均能耗约为LMAC-2协议的64.1%,为ALOHA协议的14.2%。
文摘针对PTP(precise time protocol)协议在应用层获取软件时间戳导致时钟同步精度下降的问题,提出一种基于MAC(media access control)层获取硬件时间戳的PTP同步优化方案。设计了以STM32F407微处理器为核心的PTP时钟应用平台,在MAC层实现了硬件时间戳获取,避免了由于协议栈软件处理延时产生的不确定性;针对PTP时钟晶振老化导致的时间同步偏差及网络延迟抖动问题,采用迭代方法优化了本地时钟频率调节算法,提高了频率校正精度。经实际测试,主从时钟偏差的RMS(root mean square)优于20 ns,提升了时钟同步精度。
