Speaker
Ahmed Hemani
Description
The rise of megatrend in Neural Networks has identified
ASIC (custom hardware design) as a necessity to achieve
orders of magnitude greater performance and efficiency
compared to what is feasible with GPUs and FPGAs. At
KTH, we have developed a VLSI Design method called
Synchoros VLSI Design Style that enables achieving 3-4
orders better computational efficiency compared to GPUs
and FPGAs. The most critical advantage of synchoros VLSI
Design is not just its high performance and efficiency but
the fact that it can be produced with engineering effort
comparable to CUDA programming. Additionally, synchoros
VLSI Design Style also promises to dramatically lower the
manufacturing cost as well. This talk will introduce this
design style and show how it has been applied to different
types of Neural Networks.
