Traditional von Neumann computing systems involve separate processing and memory
units. However, data movement is costly in terms of time and energy and this problem is …

Computing in memory (CIM) could be used to overcome the von Neumann bottleneck and to
provide sustainable improvements in computing throughput and energy efficiency …

At Facebook, machine learning provides a wide range of capabilities that drive many
aspects of user experience including ranking posts, content understanding, object detection …

Memristor crossbars are circuits capable of performing analog matrix-vector multiplications,
overcoming the fundamental energy efficiency limitations of digital logic. They have been …

Analog hardware accelerators, which perform computation within a dense memory array,
have the potential to overcome the major bottlenecks faced by digital hardware for data …

As the limits of transistor technology are approached, feature size in integrated circuit
transistors has been reduced very near to the minimum physically-realizable channel length …

As complementary metal–oxide–semiconductor (CMOS) scaling reaches its technological
limits, a radical departure from traditional von Neumann systems, which involve separate …

Personalized recommendation systems leverage deep learning models and account for the
majority of data center AI cycles. Their performance is dominated by memory-bound sparse …

Recent success in deep neural networks has generated strong interest in hardware
accelerators to improve speed and energy consumption. This paper presents a new type of …

Exploiting model sparsity to reduce ineffectual computation is a commonly used approach to
achieve energy efficiency for DNN inference accelerators. However, due to the tightly …