Abstract—This paper presents HotSpot—a modeling methodology for developing compact thermal models based on the popular stacked-layer packaging scheme in modern very large-scale integration systems. In addition to modeling silicon and packaging layers, HotSpot includes a high-level on-chip interconnect self-heating power and thermal model such that the thermal impacts on interconnects can also be considered during early design stages. The HotSpot compact thermal modeling approach is especially well suited for preregister transfer level (RTL) and presynthesis thermal analysis and is able to provide detailed static and transient temperature information across the die and the package, as it is also computationally efficient. Index Terms—Compact thermal model, early design stages, interconnect self-heating, temperature, VLSI. I.

Abstract—Multicore architectures are becoming the main design paradigm for current and future processors. The main reason is that multicore designs provide an effective way of overcoming instruction-level parallelism (ILP) limitations by exploiting thread-level parallelism (TLP). In addition, it is a power and complexity-effective way of taking advantage of the huge number of transistors that can be integrated on a chip. On the other hand, today’s higher than ever power densities have made temperature one of the main limitations of microprocessor evolution. Thermal management in multicore architectures is a fairly new area. Some works have addressed dynamic thermal management in bi/quad-core architectures. This work provides insight and explores different alternatives for thermal management in multicore architectures with 16 cores. Schemes employing both energy reduction and activity migration are explored and improvements for thread migration schemes are proposed. Index Terms—Multicore architectures, dynamic thermal management, activity migration, dynamic voltage, frequency scaling. 1

The evolution of microprocessors has been hindered by their increasing power consumption and the heat generation speed on-die. High temperature impairs the processor’s reliability and reduces its lifetime. While hardware level dynamic thermal management (DTM) techniques, such as voltage and frequency scaling, can effectively lower the chip temperature when it surpasses the thermal threshold, they inevitably come at the cost of performance degradation. We propose an OS level technique that performs thermalaware job scheduling to reduce the number of thermal trespasses. Our scheduler reduces the amount of hardware DTMs and achieves higher performance while keeping the temperature low. Our methods leverage the natural discrepancies in thermal behavior among different workloads, and schedule them to keep the chip temperature below a given budget. We develop a heuristic algorithm based on the observation that there is a difference in the resulting temperature when a hot and a cool job are executed in a different order. To evaluate our scheduling algorithms, we developed a lightweight runtime temperature monitor to enable informed scheduling decisions. We have implemented our scheduling algorithm and the entire temperature monitoring framework in the Linux kernel. Our proposed scheduler can remove 10.5-73.6 % of the hardware DTMs in various combinations of workloads in a medium thermal environment. As a result, the CPU throughput was improved by up to 7.6% (4.1 % on average) even under a severe thermal environment. 1

The evolution of microprocessors has been hindered by the increasing power consumption and the rate at which heat is dissipated on die. Excessive amount of heat creates reliability problems, reduces lifetime of a processor, and elevates the cost of cooling and packaging considerably. To ensure an effective control of the chip temperature, it is imperative to be able to monitor the temperature variations across the die timely and accurately. Most current techniques rely on on-chip thermal sensors to report the temperature of the processor. Unfortunately, the significant variation in chip temperature both spatially and temporally exposes the limitation of the sensors. We present a compensating approach to tracking chip temperature through an OS resident software module that generates live power and thermal profiles of the processor. We developed such a software thermal sensor (STS) in a Linux system with a Pentium 4 Northwood core. We employed highly efficient numerical methods in our model to minimize the overhead of temperature calculation. We also developed an efficient algorithm for functional unit power modeling. Our power and thermal models are calibrated and validated against on-chip sensor readings, thermal images of the Northwood heat spreader, and the thermometer measurements on the package. The resulting STS offers detailed power and temperature

Abstract — Preventing silicon chips from negative, even disastrous thermal hazards has become increasingly challenging these days; considering thermal effects early in the design cycle is thus required. To achieve this, an accurate yet fast temperature model together with an early-stage, thermally optimized, design flow are needed. In this paper, we present an improved block-based compact thermal model (HotSpot 4.0) that automatically achieves good accuracy even under extreme conditions. The model has been extensively validated with detailed finite-element thermal simulation tools. We also show that properly modeling package components and applying the right boundary conditions are crucial to making full-chip thermal models like HotSpot accurately resemble what happens in the real world. Ignoring or oversimplifying package components can lead to inaccurate temperature estimations and potential thermal hazards that are costly to fix in later designs stages. Such a full-chip and package thermal model can then be incorporated into a thermally optimized design flow where it acts as an efficient communication medium among computer architects, circuit designers and package designers in early microprocessor design stages, to achieve early and accurate design decisions and also faster design convergence. For example, the temperature-leakage interaction can be readily analyzed within such a design flow to predict potential thermal hazards such as thermal runaway. An example SoC design illustrates the importance of adopting such a thermally optimized design flow in early design stages. Index Terms — compact thermal model, early design stages, leakage, parameterized model, temperature, thermally optimized design flow. I.

All existing methods for thermal-via allocation are based on a steady-state thermal analysis and may lead to excessive number of thermal vias. This paper develops an accurate and efficient thermal-via allocation considering temporally and spatially variant thermal-power. The transient temperature is calculated using macromodel by a structured and parameterized model reduction, which generates temperature sensitivity with respect to thermalvia density. By defining a thermal-violation integral based on the transient temperature, a nonlinear optimization problem is formulated to allocate thermal-vias and minimize thermal violation integral. This optimization problem is transformed into a sequence of subproblems by Lagrangian relaxation, and each subproblem is solved by quadratic programming using sensitives from the macromodel. Experiments show that compared to the existing method using steady-state thermal analysis, our method is 126X faster to obtain the temperature profile, and reduces the number of thermal vias by 2.04X under the same temperature bound.

High on-chip temperature impairs the processor’s reliability and reduces its lifetime. Hardwarelevel dynamic thermal management (DTM) techniques can effectively constrain the chip temperature, but degrades the performance. We propose an OS-level technique that performs thermalaware job scheduling to reduce DTMs. The algorithm is based on the observation that hot and cool jobs executed in a different order can make a difference in resulting temperature. Real-system implementation in Linux shows that our scheduler can remove 10.5 % to 73.6 % of the hardware DTMs in a medium thermal environment. The CPU throughput is improved by up to 7.6 % (4.1%, on average) in a severe thermal environment.