Topics of interest, of both theoretical and practical significance, include but are not limited to:

• Micro- and System-architectures, including multi- and many-core designs, heterogeneous processors, accelerators (GPUs, vector processors, Automata processor), reconfigurable (coarse grained reconfigurable and FPGA designs) and custom processors 

• Network architectures and interconnect (including high-radix networks, optical interconnects) 

• Novel memory architectures and designs (including processors-in memory) 

• Impact of new computing paradigms on irregular workloads (including neuromorphic processors and quantum computing) 

• Modeling, simulation and evaluation of novel architectures with irregular workloads 

• Innovative algorithmic techniques 

• Combinatorial algorithms (graph algorithms, sparse linear algebra, etc.)

• Impact of irregularity on machine learning approaches

• Parallelization techniques and data structures for irregular workloads

• Data structures combining regular and irregular computations (e.g., attributed graphs)

• Approaches for managing massive unstructured datasets (including streaming data) 

• Languages and programming models for irregular workloads

• Library and runtime support for irregular workloads

• Compiler and analysis techniques for irregular workloads

• High performance data analytics applications, including graph databases