Analog and digital circuits for machine learning: Page 5 of 6

July 24, 2018 // By Avi Baum
Avi Baum, chief technology officer of Hailo (Tel Aviv, Israel), compares the underlying principles and energy considerations behind analog and digital approaches to neural network implementation and machine learning circuits.

System aspects

Thus far this discussion has been devoted to the building block level. However, overlooking the rest of the system is incomplete. A system-level analysis should account for all contributors and consider the fact that at a certain point the improvement factor of the basic processing becomes negligible. Such is the case with the energy distribution. To date, state-of-the-art solutions are struggling to achieve 0.1 to 1TOPS/W when running machine learning tasks. This is equivalent to 1 to 10pJ per operation. As mentioned earlier, since the digital implementation of a neuron plateaus at 0.1pJ then 90 to 99 percent of the energy still lies in other domains which include memory elements, control fabric and bus architecture. Therefore, to harness the potential an architecture overhaul is of the essence. The energy recovered by a transition to an analog solution alone is upper bounded by 10 percent of the total energy consumed.


The following table captures some of the key properties of the various approaches and summarizes most of the items that were mentioned briefly above.

Table 1: Comparison of analog and digital bases neural networks

Next: Conclusion

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