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This "Cluster High Memory" instance type will offer a massive 240 GB of RAM and two 120GB SSDs.
For a given memory instance, energy consumption depends on whether it is currently being accessed or not.
For example, Frederic Lardinois wrote about AWS introduction of a "Cluster High Memory" instance type that will offer a massive 240 GB of RAM and two 120 GB SSDs.
To achieve the highest power reduction possible, it is also necessary to reorganize the logical address space such that the most frequently accessed segments are grouped together and can be mapped to the same physical memory instance.
The problem statement is defined as follows: Find an allocation α of memory instances and a mapping β that assigns each application profile to exactly one memory instance such that α and β yield the lowest power consumption of all possible allocations and mappings while satisfying area and user-defined constraints.
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A commonly used method to reduce memory power consumption is splitting memory into several individual memory instances [1 6].
Fig. 5 Area requirement of instruction memory (top) and data memory (bottom) with varying limits for memory instances.
Fig. 4 Power consumptions of instruction memory (top) and data memory (bottom) with varying limits for memory instances.
Zhuge et al. [9] distribute variables between different memory instances to increase digital signal processor (DSP) performance.
For a complete model of the whole memory architecture, not only the individual memory instances but also the interconnect fabric must be accounted (cf. Section 3.1).
Increasing the number of data memories reduces power consumption more gradually, and all applications can benefit from up to eight memory instances.
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