LOGIC SYNTHESIS USING DNN

By Anmol Salvi

INTRODUCTION:

With advancements and growth in the data science industry , the current applications of DNN(Deep Neural Networks) and various other machine learning techniques are seemingly limitless . Similarly in this manner , DNN also finds it’s application in logic synthesis and can be used to better optimize the entire process and also reduce the time consumed in this process. Before looking into how DNN techniques are applied for logic synthesis applications, first certain key terms need to be discussed. They include:-

A)Design Space Complexity of ALS(Approximate Logic Synthesis):- This involves gate replacement by going through each node and performing approximation on all and also calculating the error rate at primary outputs resulting from this approximation. Thus it’s complexity analysis has 2 parts namely:-

1)Node replacement

2)Error propogation

B)Deep Neural Networks:-A DNN has an input layer along with a number of hidden layers which are basically a group of neurons and are used for feature selection. An output layer also exists. Inputs from the hidden layer are  passed through a non linear activation function which includes the relu , leaky relu and sigmoid functions among others and they help minimize error in prediction using backpropogation  and updation of features at important nodes.

IMPLEMENTATION:-The input circuit to be optimized is first mapped using the cut-based technology mapping module of ABC post which depending on the mode of optimization (eg-power), critical nodes are extracted and are then passed onto the approximation module , this module replaces a node and after consulting the trained DNN model predicts the worst error rate at a primary output. If this worst error rate doesn’t violate the already predetermined error constraint by the user , the approximation module accepts this replacement , else it undo’s the last replacement and moves onto another node. This approximation process ends when either the error budget is consumed fully or all nodes in the circuit are visited. The learning rate needs to be set to a small value and using an optimizer such as Adam is preferred. The number of epochs can be determined by checking behaviour and accuracy of the validation set , a number greater than 10 is preferred to start at. Binary cross entropy is preferred to be used as the loss function.

ADVANTAGES:-A)Saves time.

                           B)Ensures greater accuracy.

                           C)Thereby ensures better performance.

                    

CONCLUSION:-One can thus conclude that Logic synthesis using DNN is one of the many boons of the advancement of the data science industry and making the most of this technique in order to better optimize power consumption & time consumption parameters among others , is essential and can thus serve to revolutionize just how logic synthesis is carried out in the VLSI industry.

 

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