Many proposals to scale quantum technology rely on modular or distributed designs
wherein individual quantum processors, called nodes, are linked together to form one large …

Applications with low data reuse and frequent irregular memory accesses, such as graph or
sparse linear algebra workloads, fail to scale well due to memory bottlenecks and poor core …

The design space exploration of scaled-out manycores for communication-intensive
applications (eg, graph analytics and sparse linear algebra) is hampered due to either lack …

Covert channels enable information leakage between security domains that should be
isolated by observing execution differences in shared hardware. These channels can …

Sparse Matrix Dense Matrix Multiplication (SpMM) is an important kernel with application
across a wide range of domains, including machine learning and linear algebra solvers. In …

Philosophically, our approaches to acceleration focus on the extreme. We must optimise
accelerators to the maximum, leaving software to fix any hardware-software mismatches …

Traditionally, architecture prototypes are built on top of FPGA infrastructure, with two
associated problems. First, very large FPGAs are prohibitively expensive for most people …

The slowing and forecasted end of Moore's Law have forced designers to look beyond
simply adding transistors, encouraging them to employ other unused resources as a manner …

Recent works have introduced task-based parallelization schemes to accelerate graph
search and sparse data-structure traversal, where some solutions scale up to thousands of …

The performance and energy costs of coordinating and performing data movement have led
to proposals adding compute units and/or specialized access units to the memory hierarchy …