Excerpt from article: “Tradeoffs To Improve Performance, Lower Power“
In the past, developers could just wait for the next silicon node and there would be a bigger, more capable processor running on a smaller geometry technology base, able to deliver higher performance at lower power.
“As scaling has slowed, this is no longer a viable strategy,” said Russell Klein, HLS platform program director at Siemens EDA. “To meet increasing computational demands today, developers have been looking to alternative approaches. This is especially true on the edge, where there can be severe constraints on compute resources and available energy. There is always a tradeoff between having an accelerator be programmable and extracting the greatest performance and efficiency. GPUs, TPUs, and similar arrays of small processors have great flexibility in that they can be easily reprogrammed, but they leave a fair amount of performance and efficiency on the table. To achieve the absolute highest performance and efficiency, a fixed hardware accelerator is required.”
But there is a tradeoff between performance and energy efficiency. “Going as fast as possible means pushing the clock frequency and voltages to their limits. It also means adding more computational elements, more and larger cache memories, and wider data paths. All of these improve performance, but use more energy,” Klein said.
Particularly at the edge, this shift is being driven by the explosion in data and the penalties — economic, power, time — in moving all of that data to the cloud, processing it, and sending it back to the edge device. In fact, the edge buildout is a result of those factors and the need for real-time, or at least near real-time, results.
Read the entire article on SemiEngineering originally published on March 11th, 2021.