The key challenge to improving performance in the age of Dark Silicon is how
to leverage transistors when they cannot all be used at the same time. In
modern SOCs, these transistors are often used to create specialized
accelerators which improve energy efficiency for some applications by 10-1000X.
While this might seem like the magic bullet we need, for most CPU applications
more energy is dissipated in the memory system than in the processor: these
large gains in efficiency are only possible if the DRAM and memory hierarchy
are mostly idle. We refer to this desirable state as Dark Memory, and it only
occurs for applications with an extreme form of locality.
To show our findings, we introduce Pareto curves in the energy/op and
mm$^2$/(ops/s) metric space for compute units, accelerators, and on-chip
memory/interconnect. These Pareto curves allow us to solve the power,
performance, area constrained optimization problem to determine which
accelerators should be used, and how to set their design parameters to optimize
the system. This analysis shows that memory accesses create a floor to the
achievable energy-per-op. Thus high performance requires Dark Memory, which in
turn requires co-design of the algorithm for parallelism and locality, with the
hardware.

在 Dark Silicon 时代，Dark Memory 状态对于算法并行性和局部性与硬件的协同设计是实现能效和高性能的关键，并通过创建特定加速器和使用 Pareto 曲线来解决能源 / 操作和 mm2 / (ops/s）度量空间的能量性能与面积权衡问题。