Hello
I came across this paper PyPIM (Processing-in-Memory) and was wondering if anyone form the Dfinity team had seen it?
https://arxiv.org/html/2308.14007v2
I am curious if there are any plans to implement it?
Some benefits would be:
Dynamic Scaling: With PyPIM, AI models running on ICP could scale their computational needs based on demand by dynamically activating PIM capabilities. This could mean more efficient use of the blockchain’s resources, allowing for more AI tasks to be processed concurrently.
Optimized Memory Use: AI algorithms often require significant memory for operations like model training or inference. PIM can help in optimizing memory usage by allowing computations to happen in-place, reducing the need for extensive external memory.
Reduced Computational Overhead: By minimizing the need for high-bandwidth memory interfaces or complex cache systems traditionally used to bridge CPU-memory gaps, the overall computational cost for running AI on ICP might decrease.
Reduced Latency: By enabling processing in memory, AI computations can occur closer to where data resides, drastically reducing the time spent on data transfer between memory and CPU. This is particularly beneficial for machine learning models that require numerous iterations over large datasets.
Faster Inference: For AI applications like real-time decision-making systems or quick response chatbots, PyPIM could allow for near-instantaneous processing of queries, enhancing user experience in decentralized applications on ICP.
Food for thought
Kind regards
Kurt