Problem context: We continue to generate nearly 2.5 quintillion bytes per day and as such, the amounts of data that needs analyzing will only increase with time.
Problem statement: Conventional architectures are not equipped to tackle this data deluge. They expend disproportionately large fraction of time and energy in moving
data over cache hierarchy, and in instruction processing, as compared to the actual computation. Further, applications which tackle massive data tend to have high degree
of data parallelism which the narrow vector units in conventional processors fail to exploit.
My solution: In this work, I proposed the Compute Cache architecture which transforms caches into active compute units capable of performing in-place computation. This
transformation unlocks massive data-parallel compute capabilities (~100X wrt SIMD processor), as a cache is comprised of many smaller sub-arrays each of which can compute in
parallel. This also reduces data movement energy over the cache hierarchy as we can perform computation in cache without moving it towards the processor. Realizing
Compute Caches brings to forth several challenges like efficient data placement, orchestration of concurrent computation in caches, ensuring soft error reliability
and more which I address efficiently.
Result impact: My study indicates that Compute Cache enabled operations can deliver significant throughput (54X) and total energy savings (14X). For a suite of data-centric
applications, Compute Caches deliver performance improvement of 1.9X and energy savings of 2.4X while being limited by Amdahl’s law. Future studies to include a richer set
of operations that can be performed in-place in cache will, I believe, help accelerate larger fractions of applications and close the gap between potential and realized improvements.
This work was awarded best demo at Center for Future Architectures Research (CFAR) Annual Workshop 2016 which showcased nearly 50 projects in computer architecture related
topics from several leading institutions. Also, this work won the 1st place at University of Michigan CSE Graduate Students Honors Competition ; a yearly competition which
recognizes research of broad interest and exceptional quality.