Hardware architecture

 

The TU Kaiserslautern team is designing an advanced new hardware accelerator for algebraic machine learning.

This new hardware architecture contains multi-bit-array processing cores, which enable multithreaded programming. 

Bit-array processing cores have novel instruction sets, which are designed for Algebraic Machine Learning (AML). The accelerator is equipped with HBM DRAM, which provides up to 460 GB/s of memory bandwidth. Each core has a separate memory channel and can work independently from other cores. The proposed hardware architecture is implemented on a Xilinx Alveo U280 Data Centre accelerator card. The board is equipped with about one million Look-up tables (LUTs) and PCI Express 4.0 support to leverage the latest server interconnect infrastructure for high-bandwidth host processors.

What's coming next... The future work will include Processing-In-Memory

Our experiments have shown the overall throughput in an AML processing task is bounded by memory bandwidth. Also, the off-chip data access energy is playing a major role in system-level energy consumption.   Thus, Processing-In-Memory (PIM) is a promising approach to address these challenges and bridge the memory-computation gap. PIM places computational logic inside the memory to exploit minimum data movement and massive internal data parallelism. Especially interesting for AML, PIM presented considerable results for a memory-bound application like “set processing”. Sparse Crossing could use PIM by directly computing the bit-array operation inside DRAM without moving the data to the processor. It is possible to compute wide bit-array operations directly in the DRAM. In fact, bit-array operations in memory can be made arbitrarily wide by dividing each bit-array among multiple memory banks operated in parallel.

By Christian Weis from Technische Universität Kaiserslautern

 

MORE INFORMATION ABOUT ALMA:

For any questions please contact This email address is being protected from spambots. You need JavaScript enabled to view it..

 

 

 

 

We use cookies on our website. Some of them are essential for the operation of the site, while others help us to improve this site and the user experience (tracking cookies). You can decide for yourself whether you want to allow cookies or not. Please note that if you reject them, you may not be able to use all the functionalities of the site.