It has been a long time that computer architecture and systems are optimized for efficient
execution of machine learning (ML) models. Now, it is time to reconsider the relationship …

Machine learning has enabled significant benefits in diverse fields, but, with a few
exceptions, has had limited impact on computer architecture. Recent work, however, has …

Far memory, where memory accesses are non-local, has become more popular in recent
years as a solution to expand memory size and avoid memory stranding. Prior far memory …

Machine learning algorithms have shown potential to improve prefetching performance by
accurately predicting future memory accesses. Existing approaches are based on the …

Integrating compute express link (CXL) with SSDs allows scalable access to large memory
but has slower speeds than DRAMs. We present ExPAND, an expander-driven CXL …

Modern data center applications have deep software stacks, with instruction footprints that
are orders of magnitude larger than typical instruction cache (I-cache) sizes. To efficiently …

The high access latency of DRAM continues to be a performance challenge for
contemporary microprocessor systems. Prefetching is a well-established technique to …

The translation of C programs to Verilog can present significant challenges for programmers
aiming to synthesize hardware. To address these challenges, several High-Level Synthesis …

Flash-based solid state drives represent an important storage tier in today's hyperscale data
centers. Although solid state drives (SSDs) are relatively reliable, data center operators are …

The rapid development of Big Data coupled with slowing down of Moore's law has made the
memory performance a bottleneck in the von Neumann architecture. Machine learning has …