Java is becoming the main software platform for consumer and embedded devices such as mobile phones, PDAs, TV set-top boxes, and in-vehicle systems. Since many of these systems are memory constrained, it is extremely important to keep the memory footprint of Java applications under control.

Energy management is important for a spectrum of systems ranging from high-performance architectures to lowend mobile and embedded devices. With the increasing number of transistors, smaller feature sizes, lower supply and threshold voltages, the focus on energy optimization is shifting from dynamic to leakage energy. Leakage energy is of particular concern in dense cache memories that form a major portion of the transistor budget. In this work, we present several architectural techniques that exploit the data duplication across the different levels of cache hierarchy. Specifically, we employ both state-preserving (dataretaining) and state-destroying leakage control mechanisms to L2 subblocks when their data also exist in L1. Using a set of media and array-dominated applications, we demonstrate the effectiveness of the proposed techniques through cycle-accurate simulation. We also compare our schemes with the previously proposed cache decay policy. This comparison indicates that one of our schemes generates competitive results with cache decay.

The capabilities of applications executing on embedded and mobile devices are strongly influenced by memory size limitations. In fact, memory limitations are one of the main reasons that applications run slowly or even crash in embedded/mobile devices. While improvements in technology enable the integration of more memory into embedded devices, the amount memory that can be included is also limited by cost, power consumption, and form factor considerations. Consequently, addressing memory limitations will continue to be of importance. Focusing on embedded Java environments, this paper shows how object compression can improve memory space utilization. The main idea is to make use of the observation that a small set of values tend to appear in some fields of the heapallocated objects much more frequently than other values. Our analysis shows the existence of such frequent field values in the SpecJVM98 benchmark suite. We then propose two object compression schemes that eliminate/reduce the space occupied by the frequent field values. Our extensive experimental evaluation using a set of eight Java benchmarks shows that these schemes can reduce the minimum heap size allowing Java applications to execute without outof-memory exceptions by up to 24 % (14 % on an average).

Limited energy and memory resources are important constraints in the design of an embedded system. Compression is an useful and widely employed mechanism to reduce the memory requirements of the system. As the leakage energy of a memory system increases with its size and because of the increasing contribution of leakage to overall system energy, compression also has a significant effect on reducing energy consumption. However, storing compressed data / instructions has a performance and energy overhead associated with decompression at runtime. The underlying compression algorithm, the corresponding implementation of the decompression and the ability to reuse decompressed information critically impact this overhead. In this paper, we explore the influence of compression on overall memory energy using a commercial embedded Java virtual machine (JVM) and a customized compression algorithm. Our results show that compression is effective in reducing energy even when considering the runtime decompression overheads for most applications.

Energy is an important constraint for battery-operated embedded Java environments. In this work, we show how the garbage collector (GC) can be tuned to reduce the energy consumption of Java applications. In particular, we show the importance of tuning the frequency of invoking GC based on object allocation and garbage creation rates to optimize leakage energy consumption. We reduce the leakage energy by exploiting the supplygated leakage power optimization that is controlled by the GC. In this mechanism, power supply to memory banks that do not hold any useful data can be shut down. We implement a new adaptive GC mechanism within Sun's KVM that optimizes the ability to shut down more banks. An evaluation of our approach using various embedded applications shows that the adaptive garbage collection scheme is effective in reducing the system energy consumption across different hardware configurations.

Energy management is important for a spectrum of systems ranging from high-performance architectures to low-end mobile and embedded devices. With the increasing number of transistors, smaller feature sizes, lower supply and threshold voltages, the focus on energy optimization is shifting from dynamic to leakage energy. In fact, leakage energy is projected to become the dominant portion of the chip power budget for 0.10 micron technology and below. Leakage energy is of particular concern in dense cache memories that form a major portion of the transistor budget.

Abstract. As both chip densities and clock frequencies steadily rise in modern microprocessors, energy consumption is quickly joining performance as a key design constraint. Power issues are increasingly important in embedded systems, especially those found in portable devices. Much research has focused on the memory subsystems of these devices since they are a leading energy consumer. Compiler optimizations that are traditionally used to increase performance have shown much promise in also reducing cache energy consumption. In this paper we study the interaction between performance-oriented compiler optimizations and memory energy consumption and demonstrate that the best performance optimizations do not necessarily generate the best energy behavior in memory. We also show a simple metric that a power-optimizing compiler can utilize in order to capture the energy impact of potential optimizations. Next, we present heuristic algorithms that determine a suitable optimization strategy given a memory energy upper bound. Finally, we demonstrate that our strategies will gain even more importance in the future when leakage energy is expected to play an even larger role in the total energy consumption equation. 1

Memory constraint presents one of the critical challenges for embedded software writers. While circuit-level solutions based on cramming as many bits as possible into the smallest area possible are certainly important, memory-conscious software can bring much higher benefits. Focusing on an embedded Java-based environment, this paper studies potential benefits and challenges when heap memory is managed at a field granularity instead of object. This paper discusses these benefits and challenges with the help of two field-level analysis techniques. The first of these, called the field-level lifetime analysis, takes advantage of the observation that, for a given object instance, not all the fields have the same lifetime. The field-level lifetime analysis demonstrates the potential benefits of exploiting this information. Our second analysis, referred to as the disjointness analysis, is built upon the fact that, for a given object, some fields have disjoint lifetimes, and therefore, they can potentially share the same memory space. To quantify the impact of these techniques, we performed experiments with several benchmarks, and point out the important characteristics that need to be considered by application writers.

Currently, there are 23 million Java-enabled handsets with more than 50 different models from 17-plus suppliers. With the growing popularity of such devices, there is a need in the embedded industry for a set of applications for accurate indications of the performance of embedded Java solutions. Towards addressing this problem, we gather a set of 12 Java applications running on PDA and cell phone like devices. We present a machine-independent characterization of the applications, specifically focusing on their memory characteristics. Our focus is motivated by the criticality of memory size constraints and heap footprints on both performance and power budgets.

Abstract. This paper presents a simple and efficient static analysis algorithm, combined with a region allocation policy for real-time embedded Java applications. The goal of this work is to provide a static analysis mechanism efficient enough to be integrated in an assisted-development environment, and to implement region-based memory management primitives suited for resource-limited platforms such as smart cards. 1