We read every piece of feedback, and take your input very seriously.
To see all available qualifiers, see our documentation.
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
1.结合您论文中计算power的两个公式,我的理解是ANN网络(如yolov8包括卷积、归一化、下采样)功耗等于FLOPS(总)*E(MAC),经过神经元(如LIF)的总功耗等于FLOPS(经过一个神经元累加的计算量)*总发放率(fr总)*E(AC)*时间(T),您的网络总功耗等于这两者相加,对吗? 2.但是结合相关代码,代码里只有ANN网络的FLOPS(总)和fr(每次经过神经元的发放率),没有FLOPS(经过一个神经元累加的计算量),所以我想知道您具体是如何计算的?能否请您详细说明一下?感谢! (给您发了邮件可能覆盖掉了) @XinhaoLuo666
The text was updated successfully, but these errors were encountered:
您好,整个网络只有AC,不存在MAC(所有的卷积输入都是脉冲值,部分连续的卷积在推理时被重参数化为一个卷积,归一化层可以被合并到卷积层里,下采样层不消耗能量)。 具体的计算方式是,统计网络测试集上所有样本上,每一层的发放率,乘以这一层的flops以及单次flops能耗(SNN加法为0.9。比如说,SNN某一层为3×3×128的卷积核对64320320的特征图进行卷积,发放率为0.12,则flops=3×3×128×64×320×320,该层能耗为3×3×128×64×320×320×0.12×0.9
Sorry, something went wrong.
No branches or pull requests
1.结合您论文中计算power的两个公式,我的理解是ANN网络(如yolov8包括卷积、归一化、下采样)功耗等于FLOPS(总)*E(MAC),经过神经元(如LIF)的总功耗等于FLOPS(经过一个神经元累加的计算量)*总发放率(fr总)*E(AC)*时间(T),您的网络总功耗等于这两者相加,对吗?
2.但是结合相关代码,代码里只有ANN网络的FLOPS(总)和fr(每次经过神经元的发放率),没有FLOPS(经过一个神经元累加的计算量),所以我想知道您具体是如何计算的?能否请您详细说明一下?感谢!
(给您发了邮件可能覆盖掉了) @XinhaoLuo666
The text was updated successfully, but these errors were encountered: