The topics may include, physical aspects of computing including relationship between information theory and computing, thermodynamics, complexity in computing and different forms of computing (classical, quantum, nature-inspired including synapsis in brains), energy estimates of computing including, machine learning algorithms and energy estimates, In the class, we will try to address as many of the following questions as possible: What are the problems in computing from the perspectives of physics and thermodynamics? Can we recast computing to be connect natural and human devices? How energy efficient are Algorithms and Software? How to design a computer from physical first principles? How close are we to developing Artificial general Intelligence? This class being offered for the first time and intends to look at computing from multiple perspectives with the common theme of energy efficiency. The intent is to examine the underlying reasons for why computing is becoming increasingly energy intensive. In addition, the class will look at computing from the underlying physics and use lessons from nature. The current era of computing is driven by mostly general-purpose computing architectures with some specialization, artificial intelligence/machine learning (ML) applications used in manufacturing, natural language processing, scientific applications with increasing requirements such as weather prediction and protein folding, and even in Level 3 & 4 driverless cars. In this class, we will examine several trends in computing including energy, complexity of applications, algorithms, and manufacturing, we try to assess as to why the current trends are unsustainable. The class will start with the basic premise of computing as touching upon historic studies and will end with examining Artificial General Intelligence for its physical embodiment. The class will have in-person lectures complemented by guest lectures delivered by practitioners and researchers. The topics covered in this class will be expansion of the Stanford Energy Colloquium (Link: https://energy.stanford.edu/events/lecturepresentationtalk/stanford-energy-seminar-sadasivan-shankar-energy-computing)
2-3 units · Letter (ABCD/NP)
The topics may include, physical aspects of computing including relationship between information theory and computing, thermodynamics, complexity in computing and different forms of computing (classical, quantum, nature-inspired including synapsis in brains), energy estimates of computing including, machine learning algorithms and energy estimates, In the class, we will try to address as many of the following questions as possible: What are the problems in computing from the perspectives of physics and thermodynamics? Can we recast computing to be connect natural and human devices? How energy efficient are Algorithms and Software? How to design a computer from physical first principles? How close are we to developing Artificial general Intelligence? This class being offered for the first time and intends to look at computing from multiple perspectives with the common theme of energy efficiency. The intent is to examine the underlying reasons for why computing is becoming increasingly energy intensive. In addition, the class will look at computing from the underlying physics and use lessons from nature. The current era of computing is driven by mostly general-purpose computing architectures with some specialization, artificial intelligence/machine learning (ML) applications used in manufacturing, natural language processing, scientific applications with increasing requirements such as weather prediction and protein folding, and even in Level 3 & 4 driverless cars. In this class, we will examine several trends in computing including energy, complexity of applications, algorithms, and manufacturing, we try to assess as to why the current trends are unsustainable. The class will start with the basic premise of computing as touching upon historic studies and will end with examining Artificial General Intelligence for its physical embodiment. The class will have in-person lectures complemented by guest lectures delivered by practitioners and researchers. The topics covered in this class will be expansion of the Stanford Energy Colloquium (Link: https://energy.stanford.edu/events/lecturepresentationtalk/stanford-energy-seminar-sadasivan-shankar-energy-computing)
Offered in Spring 2027 at Stanford University.