Sparse matrices are widely used in graph and data analytics, machine learning, engineering
and scientific applications. This paper describes and analyzes OuterSPACE, an accelerator …

Sparse matrix-sparse matrix multiplication (spMspM) is at the heart of a wide range of
scientific and machine learning applications. spMspM is inefficient on general-purpose …

Programmable NICs have diverse uses, and there is need for a NIC platform that can offload
computation from multiple co-resident applications to many different types of substrates …

While systolic arrays are widely used for dense-matrix operations, they are seldom used for
sparse-matrix operations. In this paper, we show how a systolic array of Multiply-and …

Sparse matrix-matrix multiplication (SpGEMM) is widely used in many scientific and deep
learning applications. The highly irregular structures of SpGEMM limit its performance and …

Emerging chips with hundreds and thousands of cores require networks with unprecedented
energy/area efficiency and scalability. To address this, we propose Slim NoC (SN): a new on …

Dynamic adaptation is a post-silicon optimization technique that adapts the hardware to
workload phases. However, current adaptive approaches are oblivious to implicit phases …

Networks-on-Chip (NoCs) are currently the paradigm of choice to interconnect the cores of a
chip multiprocessor. However, conventional NoCs may not suffice to fulfill the on-chip …

With the end of Dennard scaling and Moore's law, it is becoming increasingly difficult to build
hardware for emerging applications that meet power and performance targets, while …

Due to advances in semiconductor techniques, many-core processors have been widely
used in high performance computing. However, many applications still cannot be carried out …