Hi, When I run inference.py, an error is raised after trace_command.sh is called. The error is "free(): double free detected in tcache 2". My configs are ubuntu 20.04 focal, gcc 9.3, cuda 10.1, cuDNN 7.5.0, python 3.6, pytorch 1.1.0 (according to the suggestions in the user manual). I appreciate it if you could help me resolving the issue.

Hi, I have 3 questions:

1. why the readLatencyADC is just set to numColMuxed? I expect it to be initialized to something which its dimension is time like X.readLatency. However, I found numColMuxed just as the number of columns that share same read circuit that does not have any relation with time. https://github.com/neurosim/DNN_NeuroSim_V1.3/blob/930c75772d43c733eb3090a5636761e058a16520/Inference_pytorch/NeuroSIM/SubArray.cpp#L853

2. In general, I cannot understand what is exactly the CalculateclkFreq. Can you give some description about it? https://github.com/neurosim/DNN_NeuroSim_V1.3/blob/930c75772d43c733eb3090a5636761e058a16520/Inference_pytorch/NeuroSIM/SubArray.cpp#L848

3. In the above code snippet there is an if clause. In this cluase, readLatency is calculated by summing up latency of some modules like adc, switchmatrix and so on. The problem is that, unlike the previous if clause, the CalculateLatency method of the modules is not called. So it is expected that latency of the modules, i.e. X.readLatency is equal to zero which is wrong.

@neurosim @alu75

I have noticed that, for example, torch.zeros_like() do not define a device. A significant speedup can be achieved when one is defining the device to be equal to 'cuda' in those calls:

Currently:

torch.zeros_like(outputOrignal)

Improved:

torch.zeros_like(outputOrignal, device='cuda')

The same is relevant for torch.normal.

The changes would mainly be needed to be applied to: https://github.com/neurosim/DNN_NeuroSim_V1.3/blob/3754e10e939e80b4952ba4e09a3afb7972456fc9/Inference_pytorch/modules/quantization_cpu_np_infer.py

For the purpose of signed computation, is negative weights are supported by the simulator? If so, could you please explain how it should be implemented in a simulation? Just for the context: it is very common to perform signed computation using differential conductance of a pair of memristors.

Hello, I am using DNN_NeuroSim for inference part. I am unable to find correct relation between Power consumption and Chip area.For instance, in the NeuroSim, for pretrained VGG8 the power consumption and Chip area are around 0.1 Watt and 120 mm2. Howover, in the same platforms such as ISAAC and PUMA-simulator the power consumption and Chip area are around 65 Watt and 90 mm2. I can not understant this difference. Please help me.

PUMA-simulator: https://github.com/Aayush-Ankit/puma-simulator ISAAC paper: https://www.cs.utah.edu/~rajeev/pubs/isca16.pdf

Hello @neurosim , I want to ask about the meaning of the first parameter (numRead) of the AdderTree:: CalculateLatency(void AdderTree::CalculateLatency(double numRead, int numUnitAdd, double _capLoad)), at the line 120 of AdderTree.cpp.

Question 1

At the line 694 of Chip.cpp, Gaccumulation->CalculateLatency(ceil(numTileEachLayer[1][l]netStructure[l][5](numInVector/(double) Gaccumulation->numAdderTree)), numTileEachLayer[0][l], 0); I want to know why netStructure[l][5] needs to be multiplied by numTileEachLayer[1][l].

Question 2

At the line 489 of Tile.cpp, accumulationCM->CalculateLatency((int)(numInVector/param->numBitInput)*ceil(param->numColMuxed/param->numColPerSynapse), numPE, 0); I want to know what ceil (param ->numColMixed/param ->numColPerSynapse) means. Moreover, when param->numColPerSynapse is greater than param ->numColMixed, its value (ceil (param ->numColMixed/param ->numColPerSynapse)) is 0, because both parameters are of type int. Is there a bug?

Thank you for the great tool!

I'm currently using DNN NeuroSim v1.3 to evaluate the off-chip training performance of my memristor.

Have some questions for the conductance variation in the train.py and inference.py file.

1. Should I use the conductance variation in percentage value in following code?

parser.add_argument('--vari', default=0, help='conductance variation (e.g. 0.1 standard deviation to generate random variation)')

I'm bit curious whether I should use 0.05 or 5 if i have a memristor with 5% cycle to cycle conductance variation.

2. Is the conductance variation in above argument cycle-to-cycle or device-to-device?

Again, thank you for the great tool.

I got an error code ImportError: cannot import name 'weak_script_method'. As below "quantization_cpu_np_infer" has any problem... Could you give me any advices?

Traceback (most recent call last): File "DNN_NeuroSim_V1.3/Inference_pytorch/inference.py", line 14, in from utee import hook File "/home/ubuntu/DNN_NeuroSim_V1.3/Inference_pytorch/utee/hook.py", line 5, in from modules.quantization_cpu_np_infer import QConv2d,QLinear File "/home/ubuntu/DNN_NeuroSim_V1.3/Inference_pytorch/modules/quantization_cpu_np_infer.py", line 5, in from torch._jit_internal import weak_script_method ImportError: cannot import name 'weak_script_method'

Hello! It seems that the activityColWrite, activityRowWrite of SubArray class are not initialized or assigned a value anywhere. Could you please tell me the meaning of these variables? Thank you! @alu75

After running make it is showing no errors, but the output CSV files are being generated nowhere. I am a beginner at this. Am I missing something? Please help. Thank you.

Hello

First, Thank you for Great Tool!

I have a question about SAR ADC Power Consumption formula from SarADC.cpp

May I wonder how this formula below comes? especially for log2(levelOutput) ?

Column_Power = (0.4710*log2(levelOutput)+1.9529)*1e-6;

I know that log2(levelOutput) is related to resolution of SAR ADC, but I want to know the reason why Column power increases linearly when resolution of SAR ADC increases

Looking forward to your reply!

Hi @neurosim and @alu75 , At line 718 of Chip.cpp, I found that the x_init and y_init of GhTree->CalculateLatency are always 0, which causes the condition "if (((!x_init) && (!y_init)) || ((!x_end) && (!y_end)))" in HTree::CalculateLatency to always be satisfied. And thus, at the same time, x_end and y_end become invalid variables. I'm wondering if this is a bug or if the tileLocaEachLayer is just useless.

Also, at line from 326 to 338 of Chip.cpp, in the calculation of tileLocaEachLayer in ChipFloorPlan, why thisTileTotal is not accumulated from i=0, but from i=1?

hi，我在Subarray的能耗评估处看到：SubArray.cpp row:1241 // Read readDynamicEnergyArray = 0; readDynamicEnergyArray += capBL * cell.readVoltage * cell.readVoltage * numReadCells; // Selected BLs activityColWrite readDynamicEnergyArray += capRow2 * tech.vdd * tech.vdd * numRow * activityRowRead; // Selected WL readDynamicEnergyArray *= numColMuxed; 有两个疑问，想请您解答一下：

1. 好像只计算了BL、WL的动态功耗，没有看到RRAM的器件功耗？
2. 在inference.py中看到需要中值电导来消除高阻电流带来的影响，在评估功耗时没有看到相关的部分？ 还请您在百忙之中解答一下，多谢了！ from NeuroSIm的仰慕者

Hi, Can you explain why there is a divide by 3 in the following formula: Gaccumulation->CalculateArea(NULL, globalBufferHeight/3, NONE); (the above code can be found in chip.cpp, in the ChipCalculateArea function) @neurosim

Hi, I cannot understand how the globalBusWidth is calculated? globalBusWidth += ((desiredTileSizeCM)+(desiredTileSizeCM)/paramnumColMuxed)*numTileEachLayer[0][i]*numTileEachLayer[1][i];

More specifically, my problem is with this part : (desiredTileSizeCM)/paramnumColMuxed)*numTileEachLayer[0][i]*numTileEachLayer[1][i] @neurosim

Hi, Why there is a divide by 2 in the following line? maxLayerInput += netStructure[i][IFM_LENGTH]*netStructure[i][IFM_WIDTH]*netStructure[i][IFM_CHANNEL_DEPTH]/2 (could be found in chip.cpp, ChipInitialize function) @neurosim