Memories are increasingly dominating Systems on Chip (SoC) designs and thus contribute a large percentage of the total system's power dissipation, area and reliability. In this paper, we present a tool which captures the effects of supply voltage Vddand temperature on memory performance and their interrelationships. We propose a Temperature- and Reliability- Aware Memory Design (TRAM) approach which allows designers to examine the effects of frequency, supply voltage, power dissipation, and temperature on reliability in a mutually interrelated manner. Our experimental results indicate that thermal unaware estimation of probability of error can be off by at least two orders of magnitude and up to five orders of magnitude from the realistic, temperature-aware cases. We also observed that thermal aware Vddselection using TRAM can reduce the total power dissipation by up to 2.5times while attaining an identical predefined limit on errors.

Process scaling and higher leakage power have resulted in increased power densities and elevated die temperatures. Due to the interdependence of temperature and leakage power, we observe that the floorplan has an impact on both the temperatures and the leakage of the IP-blocks in a system on chip (SoC). Hence, in this paper we propose a novel system level leakage aware floorplanner (LEAF) which optimizes floorplans for temperature-aware leakage power along with the traditional metrics of area and wire length. Our floorplanner takes a SoC netlist and the dynamic power profile of functional blocks to determine a placement while optimizing for temperature dependent leakage power, area, and wire length. To demonstrate the effectiveness of LEAF, we implemented our methodology on ten industrial SoC designs from Freescale Semiconductor Inc. and evaluated the trade-off between leakage power and area. We observed up to 190% difference in the leakage power between leakage-unaware and leakage aware floorplanning.

This paper presents a memory binding algorithm for behaviors, used in application-specific integrated circuits (ASICs), that are characterized by the presence of conditionals and deeply nested loops that access memory extensively through arrays. Unlike previous works, this algorithm examines the effects of branch probabilities and allocation constraints. First, we demonstrate, through examples, the importance of incorporating branch probabilities and allocation constraint information when searching for a performance-efficient memory binding. We also show the interdependence of these two factors and how varying one without considering the other may greatly affect the performance of the behavior. Second, we introduce a memory binding algorithm that has the ability to examine numerous bindings by employing an efficient performance estimation procedure. The estimation procedure exploits locality of execution, which is an inherent characteristic of target behaviors. This enables the performance estimation technique to look at the global impact of the different bindings, given the allocation constraints. We tested our algorithm using a number of benchmarks from the parallel computing domain. A series of experiments demonstrates the algorithm's ability to produce bindings that optimize performance, meet memory allocation constraints, and adapt to different resource constraints and branch probabilities. One limitation of our algorithm is that, in its current form, it is not well suited for system-on-a-chip synthesis where there is complex communication between general-purpose microprocessors that use custom-designed arrays. Results show that the algorithm requires 41% fewer memories with a performance loss of only 0.2% when compared to a parallel memory architecture. When compared to the best of a series of random memory bindings, the algorithm improves schedule performance by 22%.

This paper proposes a novel circuit design methodology, called common-case computation (CCC)-based design, and new design automation algorithms for optimizing energy consumption and performance. The proposed techniques are applicable in conjunction with any high-level design methodology, where a structural register-transfer level (RTL) description and its corresponding scheduled behavioral (cycle-accurate functional) description are available. It is a well-known fact that in behavioral descriptions of hardware circuits (and also in software programs), a small set of computations often account for most of the computational complexity. However, in the hardware implementations (structural RTL or lower level), the common cases and the remaining computations are typically treated alike. This paper shows that identifying and exploiting common cases during the design process can lead to implementations that are much more efficient in terms of energy consumption and performance.

While array structures are a significant source of power dissipation, there is a lack of accurate high-level power estimators that account for varying array circuit implementation styles. We present a methodology and a tool, the implementation-dependent array power (IDAP) estimator, that model power dissipation in SRAM-based arrays accurately based on a high-level description of the array. The models are parameterized by the array operations and various technology dependent parameters. The methodology is generic and the IDAP tool has been validated on industrial designs across a wide variety of array implementations in the e500 processor core (e500 is the Motorola processor core that is compliant with the PowerPC Book E architecture). For these industrial designs, IDAP generates high-level estimates for dynamic power dissipation that are accurate with an error margin of less than 22.2% of detailed (layout extracted) SPICE simulations. We apply the tool in three different scenarios: 1) identifying the subblocks that contribute to power significantly; 2) evaluating the effect of bitline-voltage swing on array power; and 3) evaluating the effect of memory bit-cell dimensions on array power.

This paper presents a high-level leakage power analysis and reduction algorithm. The algorithm uses device-level models for leakage to precharacterize a given register-transfer level module library. This is used to estimate the power consumption of a circuit due to leakage. The algorithm can also identify and extract the frequently idle modules in the datapath, which may be targeted for low-leakage optimization. Leakage optimization is based on the use of dual threshold voltage (V/sub T/) technology. The algorithm prioritizes modules giving a high-level synthesis system an indication of where most gains for leakage reduction may be found. We tested our algorithm using a number of benchmarks from various sources. We ran a series of experiments by integrating our algorithm into a low-power high-level synthesis system. In addition to reducing the power consumption due to switching activity, our algorithm provides the high-level synthesis system with the ability to detect and reduce leakage power consumption, hence, further reducing total power consumption. This is shown over a number of technology generations. The trend in these generations indicates that leakage becomes the dominant component of power at smaller feature size and lower supply voltages. Results show that using a dual-V/sub T/ library during high-level synthesis can reduce leakage power by an average of 58% for the different technology generations. Total power can be reduced by an average of 15.0%-45.0% for 0.18-0.07 /spl mu/m technologies, respectively. The contribution of leakage power to overall power consumption ranges from 22.6% to 56.2%. Our approach reduced these values to 11.7%-26.9%.

This paper presents a clock selection algorithm for control-flow intensive behaviors that are characterized by the presence of conditionals and deeply nested loops. Unlike previous papers, which are primarily geared toward data-dominated behaviors, this algorithm examines the effects of branch probabilities and their interaction with allocation constraints. Using examples, we demonstrate, how changing branch probabilities and resource allocation can dramatically affect the optimal clock period, and hence, the performance of the schedule, and show that the interaction of these two factors must also be taken into account when searching for an optimal clock period. We then introduce the clock selection algorithm, which employs a fast critical-path analysis engine that allows it to evaluate what effect different clock periods, branch probabilities, and resource allocations may ultimately have on the performance of the behavior. When evaluating the critical path, we exploit the fact that our target behaviors exhibit locality of execution. We tested our algorithm using a number of benchmarks from various sources. A series of experiments demonstrates that our algorithm is quickly capable of selecting a small set of performance-enhancing clock periods, among which the optimal clock period typically lies. Another experiment demonstrates that the algorithm can adapt to varying resource constraints.

This paper presents a high-level leakage power analysis and reduction algorithm. The algorithm uses device-level models for leakage to pre-characterize a given register-transfer level module library. This is used to estimate the power consumption of a circuit due to leakage. The algorithm can also identify and extract the frequently idle modules in the datapath, which may be targeted for low-leakage optimization. Leakage optimization is based on the use of dual threshold voltage (V/sub T/) technology. The algorithm prioritizes modules giving a high level synthesis (HLS) system an indication of where most gains for leakage reduction may be found. Results show that using a dual-V/sub T/ library during HLS can reduce leakage power by an average of 59% for the different technology generations. Total power can be reduced by an average of 18.8% to 45.4% for 0.18 /spl mu/m to 0.07 /spl mu/m technologies, respectively, compared to register-transfer level (RTL) circuits optimized for switching power only. The contribution of leakage power to overall power consumption of switching power optimized RTL circuits ranges from 23.5% to 54.1%. Our approach reduced these values to 11.4% to 25.9%.

This paper presents a design methodology, called common-case computation (CCC), and new design automation algorithms for optimizing power consumption or performance. The proposed techniques are applicable in conjunction with any high-level design methodology where a structural register-transfer level (RTL) description and its corresponding scheduled behavioral (cycle-accurate functional RTL) description are available. It is a well-known fact that in behavioral descriptions of hardware (also in software), a small set of computations (CCCs) often accounts for most of the computational complexity. However, in hardware implementations (structural RTL or lower level), CCCs and the remaining computations a typically treated alike. This paper shows that identifying and exploiting CCCs during the design process can lead to implementations that are much more efficient in terms of power consumption or performance. We propose a CCC-based high-level design methodology with the following steps: extraction of common-case behaviors and execution conditions from the scheduled description, simplification of the common-case behaviors in a stand-alone manner, synthesis of common-case detection and execution circuits from the common-case behaviors, and composing the original design with the common-case circuits, resulting in a CCC-optimized design. We demonstrate that CCC-optimized designs reduce power consumption by up to 91.5%, or improve performance by up to 76.6% compared to designs derived without special regard for CCCs.

The paper presents a memory binding algorithm for behaviors that are characterized by the presence of conditionals and deeply-nested loops that access memory extensively through arrays. Unlike previous works, this algorithm examines the effects of branch probabilities and allocation constraints. First, we demonstrate through examples, the importance of incorporating branch probabilities and allocation constraint information when searching for a performance-efficient memory binding. We also show the interdependence of these two factors and how varying one without considering the other may greatly affect the performance of the behavior. Second, we introduce a memory binding algorithm that has the ability to examine numerous bindings by employing an efficient performance estimation procedure. The estimation procedure exploits locality of execution, which is an inherent characteristic of target behaviors. This enables the performance estimation technique to look at the global impact of the different bindings, given the allocation constraints. We tested our algorithm using a number of benchmarks from the parallel computing domain. A series of experiments demonstrates the algorithm's ability to produce bindings that optimize performance, meet memory allocation constraints, and adapt to different resource constraints and branch probabilities. Results show that the algorithm requires 37% fewer memories with a performance loss of only 0.3% when compared to a parallel memory architecture. When compared to the best of a series of random memory bindings, the algorithm improves schedule performance by 21%.

In this paper, we present a comprehensive high-level synthesis system that is geared toward reducing power consumption in control-flow intensive as well as data-dominated circuits. An iterative improvement framework allows the system to search the design space by examining the interaction between the different high-level synthesis tasks. In addition to incorporating traditional high-level synthesis tasks such as scheduling, module selection and resource sharing, we introduce a new optimization that performs power-conscious structuring of multiplexer networks, which are predominant in control-flow intensive circuits. The scheduler employed is capable of loop optimizations within and across loop boundaries. We also introduce a fast power estimation technique, based on switching activity matrices, to drive the synthesis process. Experimental results for a number of control-flow intensive and data-dominated benchmarks demonstrate power reduction of up to 62% (58%) when compared to V/sub dd/-scaled area-optimized (delay-optimized) designs. The area overheads over area-optimized designs are less than 39%, whereas the area savings over delay-optimized designs are up to 40%.

In this paper, we present a novel scheduling algorithm targeted toward minimizing the average execution time of control-flow intensive behavioral descriptions. Our algorithm uses a control/data flow graph model, which preserves the parallelism inherent in the application. It explores previously unexplored regions of the solution space by its ability to overlap the schedules of independent iterative constructs, whose bodies share resources. It also incorporates well known optimization techniques like loop unrolling in a natural fashion. This is made possible by a general loop-handling technique, which we have devised. Application of the algorithm to several common benchmarks demonstrates up to 4.8-fold improvement in expected schedule length over existing scheduling algorithms, without paying a price in terms of the best and worst case schedule lengths required to execute the behavioral description (in fact, frequently, the best/worst case schedule lengths are also better for our algorithm).

In this paper, we present a comprehensive high-level synthesis system that is geared towards reducing power consumption in control-flow intensive circuits. An iterative improvement algorithm is at the heart of the system. The algorithm searches the design space by handling scheduling, module selection, resource sharing and multiplexer network restructuring simultaneously. The scheduler performs concurrent loop optimization and implicit loop unrolling. It minimizes the expected number of cycles of the schedule without compromising on the minimum and maximum schedule lengths. A fast simulation technique based on trace manipulation aids power estimation in driving synthesis in the right direction. Experimental results demonstrate power reduction of up to 85% with minimal overhead in area over area-optimized designs operating at 5 V.

In this paper, we present a power estimation technique for control-flow intensive designs that is tailored towards driving iterative high-level synthesis systems, where hundreds of architectural trade-offs are explored and compared. Our method is fast and relatively accurate. The algorithm utilizes the behavioral information to extract branch probabilities, and uses these in conjunction with switching activity and circuit capacitance information, to estimate the power consumption of a given architecture.

Presents a novel scheduling algorithm targeted towards minimizing the average execution time of control-flow intensive behavioral descriptions. Our algorithm uses a control-data flow graph (CDFG) model, which preserves the parallelism inherent in the application. It explores previously unexplored regions of the solution space through its ability to overlap the schedules of independent iterative constructs whose bodies share resources. It also incorporates well-known optimization techniques like loop unrolling in a natural fashion. This is made possible by a general loop-handling technique which we have devised. Application of the algorithm to several common benchmarks demonstrates up to 4.8-fold improvement in expected schedule length over existing scheduling algorithms, without paying a price in terms of the best- and worst-case schedule lengths required to execute the behavioral description (in fact, frequently, the best/worst-case schedule lengths are also better for our algorithm).