为满足医学系统芯片(SOC)的低成本、低功耗、微型化的需求,定制了一款兼容AHB总线接口的NorFlash控制器IP.该设计针对常规Flash控制器功能繁杂,读写数据需长时间等待等缺点,采用了硬件解锁、简化块擦除模块和增加写操作数据寄存器等优...为满足医学系统芯片(SOC)的低成本、低功耗、微型化的需求,定制了一款兼容AHB总线接口的NorFlash控制器IP.该设计针对常规Flash控制器功能繁杂,读写数据需长时间等待等缺点,采用了硬件解锁、简化块擦除模块和增加写操作数据寄存器等优化设计方法.该设计最后进行了FPGA原型验证并进行了流片,验证测试结果表明,该IP功能正确,总线的利用率得到了提高.在系统时钟10MHz下,选用S29L V008J Nor Flash芯片,按连续存储16个32位数据计算,本设计比常规设计减少总线占用时间165μs,设计达到了预期结果.展开更多
With the rapid development of machine learning,the demand for high-efficient computing becomes more and more urgent.To break the bottleneck of the traditional Von Neumann architecture,computing-in-memory(CIM)has attra...With the rapid development of machine learning,the demand for high-efficient computing becomes more and more urgent.To break the bottleneck of the traditional Von Neumann architecture,computing-in-memory(CIM)has attracted increasing attention in recent years.In this work,to provide a feasible CIM solution for the large-scale neural networks(NN)requiring continuous weight updating in online training,a flash-based computing-in-memory with high endurance(10^(9) cycles)and ultrafast programming speed is investigated.On the one hand,the proposed programming scheme of channel hot electron injection(CHEI)and hot hole injection(HHI)demonstrate high linearity,symmetric potentiation,and a depression process,which help to improve the training speed and accuracy.On the other hand,the low-damage programming scheme and memory window(MW)optimizations can suppress cell degradation effectively with improved computing accuracy.Even after 109 cycles,the leakage current(I_(off))of cells remains sub-10pA,ensuring the large-scale computing ability of memory.Further characterizations are done on read disturb to demonstrate its robust reliabilities.By processing CIFAR-10 tasks,it is evident that~90%accuracy can be achieved after 109 cycles in both ResNet50 and VGG16 NN.Our results suggest that flash-based CIM has great potential to overcome the limitations of traditional Von Neumann architectures and enable high-performance NN online training,which pave the way for further development of artificial intelligence(AI)accelerators.展开更多
文摘为满足医学系统芯片(SOC)的低成本、低功耗、微型化的需求,定制了一款兼容AHB总线接口的NorFlash控制器IP.该设计针对常规Flash控制器功能繁杂,读写数据需长时间等待等缺点,采用了硬件解锁、简化块擦除模块和增加写操作数据寄存器等优化设计方法.该设计最后进行了FPGA原型验证并进行了流片,验证测试结果表明,该IP功能正确,总线的利用率得到了提高.在系统时钟10MHz下,选用S29L V008J Nor Flash芯片,按连续存储16个32位数据计算,本设计比常规设计减少总线占用时间165μs,设计达到了预期结果.
基金This work was supported by the National Natural Science Foundation of China(Nos.62034006,92264201,and 91964105)the Natural Science Foundation of Shandong Province(Nos.ZR2020JQ28 and ZR2020KF016)the Program of Qilu Young Scholars of Shandong University.
文摘With the rapid development of machine learning,the demand for high-efficient computing becomes more and more urgent.To break the bottleneck of the traditional Von Neumann architecture,computing-in-memory(CIM)has attracted increasing attention in recent years.In this work,to provide a feasible CIM solution for the large-scale neural networks(NN)requiring continuous weight updating in online training,a flash-based computing-in-memory with high endurance(10^(9) cycles)and ultrafast programming speed is investigated.On the one hand,the proposed programming scheme of channel hot electron injection(CHEI)and hot hole injection(HHI)demonstrate high linearity,symmetric potentiation,and a depression process,which help to improve the training speed and accuracy.On the other hand,the low-damage programming scheme and memory window(MW)optimizations can suppress cell degradation effectively with improved computing accuracy.Even after 109 cycles,the leakage current(I_(off))of cells remains sub-10pA,ensuring the large-scale computing ability of memory.Further characterizations are done on read disturb to demonstrate its robust reliabilities.By processing CIFAR-10 tasks,it is evident that~90%accuracy can be achieved after 109 cycles in both ResNet50 and VGG16 NN.Our results suggest that flash-based CIM has great potential to overcome the limitations of traditional Von Neumann architectures and enable high-performance NN online training,which pave the way for further development of artificial intelligence(AI)accelerators.
