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- Dec 19, 2005
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You'd think there'd only be great things with HBM performance going forward! You'd be wrong?
"How does this create a new problem? The static power draw for the DRAM core will remain essentially the same and will increase with capacity. HBM is more power-efficient in terms of bits per watt, and enables higher transfer rates. Total power and energy may increase significantly if the memory is being used at full capacity.
Then we look at the impact on compute. “The major challenge with all most designs is that they can put in lots of compute, and they can add more compute, more parallelism, but it is actually a compute and memory problem,” says Ron Lowman, strategic marketing manager at Synopsys. “Systems have been constrained by bandwidth and fighting that bottleneck to memory.”
So what happens when that limitation goes away? “HBM provides an unprecedented amount of bandwidth between the CPU and memory,” says Cadence’s Greenberg. “HBM2E provides 2.4Tbit/s of bandwidth, with further specification enhancements on the horizon. By using an interposer-based technology, the energy-per-bit is kept low, but power — the product of energy-per-bit and number of bits transferred per second — may be relatively high at terabits-per-second transmission rates.”
This starts to cause some new problems. “One of these includes accounting for the power noise impact from HBM I/Os,” says Calvin Chow, senior area technical manager at ANSYS. “Even though the power per pin is lower, there are many more I/Os firing in parallel, resulting in significant increase in current consumption. Though the signal traces are shorter, there is still a noise concern due to the simultaneous switching of a large number of I/Os.”"
https://semiengineering.com/hbm-issues-in-ai-systems/
"How does this create a new problem? The static power draw for the DRAM core will remain essentially the same and will increase with capacity. HBM is more power-efficient in terms of bits per watt, and enables higher transfer rates. Total power and energy may increase significantly if the memory is being used at full capacity.
Then we look at the impact on compute. “The major challenge with all most designs is that they can put in lots of compute, and they can add more compute, more parallelism, but it is actually a compute and memory problem,” says Ron Lowman, strategic marketing manager at Synopsys. “Systems have been constrained by bandwidth and fighting that bottleneck to memory.”
So what happens when that limitation goes away? “HBM provides an unprecedented amount of bandwidth between the CPU and memory,” says Cadence’s Greenberg. “HBM2E provides 2.4Tbit/s of bandwidth, with further specification enhancements on the horizon. By using an interposer-based technology, the energy-per-bit is kept low, but power — the product of energy-per-bit and number of bits transferred per second — may be relatively high at terabits-per-second transmission rates.”
This starts to cause some new problems. “One of these includes accounting for the power noise impact from HBM I/Os,” says Calvin Chow, senior area technical manager at ANSYS. “Even though the power per pin is lower, there are many more I/Os firing in parallel, resulting in significant increase in current consumption. Though the signal traces are shorter, there is still a noise concern due to the simultaneous switching of a large number of I/Os.”"
https://semiengineering.com/hbm-issues-in-ai-systems/