Field Programmable Gate Arrays (FPGAs) are spatial architectures with a heterogeneous
reconfigurable fabric. They are state-of-the-art for prototyping, telecommunications …

Sparse-sparse matrix multiplication (SpGEMM) is a computation kernel widely used in
numerous application domains such as data analytics, graph processing, and scientific …

Enabled by modern languages and retargetable compilers, software development is in a
virtual “Cambrian explosion” driven by a critical mass of powerfully parameterized libraries; …

Tensor factorizations are powerful tools in many machine learning and data analytics
applications. Tensors are often sparse, which makes sparse tensor factorizations memory …

While research has shown that the agile chip design methodology is promising to sustain
the scaling of computing performance in a more efficient way, it is still of limited usage in …

Neural network models, celebrated for their outstanding scalability and computational
capabilities, have demonstrated remarkable performance across various fields such as …

Field-programmable gate arrays (FPGAs) provide an opportunity to co-design applications
with hardware accelerators, yet they remain difficult to program. High-level synthesis (HLS) …

Reconfigurable accelerator fabrics, including coarse-grain reconfigurable arrays (CGRAs),
have experienced a resurgence in interest because they allow fast-paced software algorithm …

Coarse-grained reconfigurable arrays (CGRAs), loosely defined as arrays of functional units
(eg, adder, subtractor, multiplier, divider, or larger multi-operation units, but smaller than a …

We introduce PyRTL, a Python embedded hardware design language that helps concisely
and precisely describe digital hardware structures. Rather than attempt to infer a good …