Synthesis Techniques for Power-Efficient Integrated Circuits
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In the past few years, power efficiency has been increasingly important for integrated circuits. As the Moore’s law effects slows down, the improvement of power consumption through scaling of silicon process technology is hitting the limits. At the same time, IC chips are more often embedded into mobile devices, which usually have no outer continuous power supply. The power efficiency is even more critical due to the limited electricity stored in batteries of these mobile devices. Besides, the high-performance ICs used in server farms or data centers also require improved power efficiency to alleviate the heat dissipation of the chips, which causes additional cost to lower the temperature of the facilities. The profit of crypto-currency mining is even directly affected by the electrical energy consumption of the mining hardware including ASICs, GPUs and FPGAs, which accounts for the largest part of the cost. Thus, more techniques for power efficiency were exploited in recent years to achieve further power reduction in addition to that achieved by silicon process advancements. Among the techniques for improving power efficiency, approximate computing has been recognized as an effective low power technique for applications with intrinsic error tolerance, such as image processing and machine learning. Existing efforts on this are mostly focused on approximate circuit design, approximate logic synthesis or processor architecture approximation techniques. Chapter 2 of this research aims to make good use of approximate circuits at system and block levels. In particular, approximation aware scheduling, functional unit allocation and binding algorithms are developed for data intensive applications. Simple yet credible error models, essential for precision control in the optimizations, are investigated. The algorithms are further extended to include bitwidth optimization in fixed point computations. Experimental results, including those from Verilog simulations, indicate that the proposed techniques facilitate desired energy savings under latency and accuracy constraints. With their flexibility in allowing reconfiguration for different applications, hardware such as FPGAs have become increasingly preferred over ASICs as a platform for high-performance comii puting like accelerators. However, this advantage is partially defeated by the time-intensive highlevel synthesis (HLS) process and the poor controllability for the synthesized architecture. We propose a fast mapping-based high level synthesis technique friendly to local incremental change. It exploits the SSA (Static Single Assignment) form with array SSA extension and ϕ-function based flow control. It first maps the SSA form based IR to a fully pipelined circuit, then alters the circuit to a partially pipelined or nonpipelined circuit by resource sharing in an optional phase of resource optimization. Pipeline interlocking to address the pipeline hazards is also provided, which has better power-efficiency. Adaptive Supply Voltage (ASV) is another power-efficient approach to achieving resilience against process variation and circuit aging. Fine-grained ASV offers further power efficiency gains, but entails relatively complex control circuit, which has not been well studied yet. Chapter 4 of this research presents two control design techniques: one is rule-based control derived from network flow optimization and the other is finite state machine control. For the FSM control, a graph-based algorithm that automates the control vector generation is proposed. This research also presents an iterative greedy heuristic for delay sensor deployment in ASV designs. The effectiveness of these techniques is confirmed by experiments performed on ICCAD 2014 benchmark circuits. The results show that our techniques achieve around 20% leakage power reduction compared to coarsegrained ASV, while maintain about the same timing yield.
Li, Chaofan (2018). Synthesis Techniques for Power-Efficient Integrated Circuits. Doctoral dissertation, Texas A & M University. Available electronically from