An Energy-Efficient Partitioned Instruction Cache Architecture for Embedded Processors

Microarchitects should consider energy consumption, together with performance, when designing instruction cache architecture, especially in embedded processors. This paper proposes a power-aware instruction cache architecture, named Partitioned Instruction Cache (PI-Cache), to reduce dynamic energy consumption in the instruction cache. The proposed PI-Cache is composed of several small sub-caches. When the PI-Cache is accessed, only one sub-cache is accessed by utilizing the locality of applications. In the meantime, the other sub-caches are not accessed, resulting in dynamic energy reduction. The PI-Cache also reduces energy consumption by eliminating energy consumed in tag matching. Moreover, performance loss is little, considering the physical cache access time. We evaluated the energy efficiency by running cycle accurate simulator, SimpleScalar, with power parameters obtained from CACTI. Simulation results show that the PI-Cache reduces dynamic energy consumption by 42% – 59%.

High Energy efficiency and high performance are the key regiments for Internet of Things (IoT) edge devices. Exploiting cluster of multiple programmable processors has recently emerged as a suitable solution to address this challenge. However, one of the main power bottlenecks for multi-core architectures is the instruction cache memory. We propose a two-level structure based on Standard Cell Memories (SCMs) which combines a private instruction cache (L1) per-core and a low-latency (only one cycle latency) shared instruction cache (L1,5). We present a detailed comparison of performance and energy efficiency for different instruction cache architectures. Our system-level analysis shows that the proposed design improves upon both state-of-the art private and shared cache architectures and balances well performance with energy-efficacy. On average, when executing a set of real-life IoT applications, our multi-level cache improves performance and energy efficiency both by 10% with respect to the private instruction cache system, and improves energy efficiency by 15% and 7% with a performance loss of only 2% with respect to the shared instruction cache. Besides, relaxed timing makes two-level instruction cache an attractive choice for aggressive implementation, with more slack for convergence in physical design.

Very small instruction caches have been shown to greatly reduce fetch energy. However, for many applications the use of a small filter cache can lead to an unacceptable increase in execution time. In this paper, we propose the tagless hit instruction cache (TH-IC), a technique for completely eliminating the performance penalty associated with filter caches, as well as a further reduction in energy consumption due to not having to access the tag array on cache hits. Using a few metadata bits per line, we are able to more efficiently track the cache contents and guarantee when hits will occur in our small TH-IC. When a hit is not guaranteed, we can instead fetch directly from the L1 instruction cache, eliminating any additional cycles due to a TH-IC miss. Experimental results show that the overall processor energy consumption can be significantly reduced due to the faster application running time and the elimination of tag comparisons for most of the accesses.

The energy consumed in instruction fetching accounts for a significant portion of total processor energy consumption. Energy consumption as well as performance should be considered when designing high performance embedded processors. In this paper, we present a hardware-based loop detection technique to reduce the energy consumption in the instruction fetch unit (instruction cache and branch prediction logic) for high performance embedded processors. The proposed instruction fetch unit reduces the energy consumed in the instruction cache by replacing the accesses to the large main instruction cache with those to the small selectively accessed cache (SAC). It also reduces the energy consumed in the branch prediction logic by reducing unnecessary accesses to the branch prediction logic. We evaluate the proposed design using a simulation infrastructure based on SimpleScalar and CACTI. Simulation results show that the proposed technique reduces the energy consumption in the instruction cache and the branch prediction logic by 20% and 24% on the average, respectively. Moreover, the proposed scheme shows little performance loss compared to the traditional scheme.

Demands for low-energy microcontrollers have been increasing in recent years. Since most microcontrollers achieve user programmability by integrating nonvolatile (NV) memories such as flash memories for storing their programs, the large power consumption required in accessing an NV memory has become a major problem. This problem becomes critical when the power supply voltage of NV microcontrollers is decreased. We can solve this problem by introducing an instruction cache, thus reducing the access frequency of the NV memory. Unlike general-purpose microprocessors, microcontrollers used for real-time applications in embedded systems must accurately calculate program execution time prior to its execution. Therefore, we introduce a "transparent" instruction cache, which does not change the existing NV microcontroller's cycle-level execution time, for reducing power and energy consumption, but not for improving the processing speed. We have conducted detailed microar chitecture design based on the architecture of a major industrial microcontroller, and we evaluated power and energy consumption for several benchmark programs. Our evaluation shows that the proposed instruction cache can successfully reduce energy consumption in a fairly wide range of practical NV microcontroller configurations.

Instruction caches typically consume 27% of the total power in modern high-end embedded systems. We propose a compiler-managed instruction store architecture (K-store) that places the computation intensive loops in a scratch-pad like SRAM memory and allocates the remaining instructions to a regular instruction cache. At runtime, execution is switched dynamically between the instructions in the traditional instruction cache and the ones in the K-store, by inserting jump instructions. The necessary jump instructions add 0.038% on an average to the total dynamic instruction count. We compare the performance and energy consumption of our K-store with that of a conventional instruction cache of equal size. When used in lieu of a 8KB, 4-way associative instruction cache, K-store provides 32% reduction in energy and 7% reduction in execution time. Unlike loop caches, K-store maps the frequent code in a reserved address space and hence, it can switch between the kernel memory and the instruction cache without any noticeable performance penalty.

Energy consumption is one of the most important issues in resource-constrained embedded systems. Many such systems run Java-based applications due to Java's architecture-independent format (bytecode). Standard techniques for executing bytecode programs, e.g. interpretation or just-in-time compilation, have performance or memory issues that make them unsuitable for resource-constrained embedded systems.A superoperator-extended, lightweight Java Virtual Machine (JVM) can be used in resource-constrained embedded systems to improve performance and reduce memory consumption. This paper shows that such a JVM also significantly reduces energy consumption. This is due primarily to a considerable reduction in the number of memory accesses and thus in energy consumption in the instruction and data TLBs and caches and, in most cases, in DRAM energy consumption. Since the fraction of processor energy dissipated in these units is approximately 60%, the energy savings achieved are significant.The paper evaluates the number of load, store, and computational instructions eliminated by the use of proposed superoperators as compared to a simple interpreter on a set of embedded benchmarks. Using cache and DRAM per access energy we estimate the total processor/DRAM energy saved by using our JVM. Our results show that with 32KB caches the reduction in energy consumption ranges from 40% to 60% of the overall processor, plus DRAM energy. Even higher savings may be achieved with smaller caches and increased access to DRAM as DRAM access energy is fairly high.

In this paper we present a methodology to enable the design of power efficient instruction cache for embedded processors. The proposed technique, which splits the instruction cache into several small sub-caches, utilizes the locality of applications to reduce dynamic energy consumption in the instruction cache. The proposed cache reduces dynamic energy consumption by accessing only one sub-cache when a request comes into the cache. It also reduces dynamic energy consumption by eliminating the energy consumed in tag matching. In addition, we propose the technique to reduce leakage energy consumption in the proposed cache. We evaluate the design using a simulation infrastructure based on SimpleScalar and CACTI. Simulation results show that the proposed cache reduces dynamic energy by 42% – 59% and reduces leakage energy by 70% – 80%.

Energy intensity is one of the most important energy feature that has a dramatic value in energy system of Iran. Indeed, Iran is one of the most energy intensive countries in the world and its main reason is related to high energy consumption in household section. In this article, we present a non-linear model that considers three scenarios in management of household energy demand reducing. Therefore, a rational percentage of energy consumption reduction in the household sector, which firstly eliminates the imbalance between energy production and consumption, and secondly, derives a rational amount of profit from various reduction scenarios, will be presented. The mentioned rational profits in this article are obtained from three scenarios. In the first scenario, it is assumed that the percentage reduction in household energy consumption will be allocated to reducing energy demand in the industrial sector, resulting in profits from value-added creation in this sector. In the second scenario, all benefits from reducing energy consumption in the household sector will be devoted to energy exports, yielding profits from this source. Finally, in the third scenario, the reduction in energy consumption will lead to a decrease in energy supply and consequently a reduction in energy supply costs. To conduct a comprehensive study, a combination of the mentioned scenarios has also been modeled and investigated. The model results indicate that with a 25% reduction in household energy consumption in the 2024-2034 timeframe, the energy imbalance will be eliminated, and allocating 5% of this reduction entirely to the industrial sector will result in profits equivalent to $164.18 billion. However, it should be noted that in the considered combined scenarios, the greater the share of the first scenario, the higher the resulting profit, and the optimal point is achieved in the first scenario.

Instruction caches are responsible for a high percentage of the chip energy consumption, becoming a critical issue for battery-powered embedded devices. We can potentially reduce the energy consumption of the first level instruction cache (L1-I) by decreasing its size and associativity. However, demanding applications may suffer a dramatic performance degradation, specially in superscalar multi-threaded processors, where, in each cycle, multiple threads access the L1-I to fetch instructions.We introduce iLP-NUCA (Instruction Light Power NUCA), a new instruction cache that substitutes the conventional L2, improving the Energy-Delay of the system. iLP-NUCA adds a new tree-based transport network topology that reduces latency and energy consumption, regarding former LP-NUCA implementations.With iLP-NUCA we reduce the size of the L1-I outperforming conventional cache hierarchies, and reducing the overall consumption, independently of the number of threads.KeywordsTransport NetworkInstruction CacheCache HierarchyBranch PredictorVictim BlockThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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The Instruction Register File (IRF) is an architectural extension for providing improved access to frequently occurring instructions. An optimizing compiler can exploit an IRF by packing an application's instructions, resulting in decreased code size, reduced energy consumption and improved execution time primarily due to a smaller footprint in the instruction cache. The nature of the IRF also allows the execution of packed instructions to overlap with instruction fetch, thus providing a means for tolerating increased fetch latencies, like those experienced by encrypted ICs as well as the presence of low-power L0 caches. Although previous research has focused on the direct benefits of instruction packing, this paper explores the use of increased fetch bandwidth provided by packed instructions. Small L0 caches improve energy efficiency but can increase execution time due to frequent cache misses. We show that this penalty can be significantly reduced by overlapping the execution of packed instructions with miss stalls. The IRF can also be used to supply additional instructions to a more aggressive execution engine, effectively reducing dependence on instruction cache bandwidth. This can improve energy efficiency, in addition to providing additional flexibility for evaluating various design tradeoffs in a pipeline with asymmetric instruction bandwidth. Thus, we show that the IRF is a complementary technique, operating as a buffer tolerating fetch bottlenecks, as well as providing additional fetch bandwidth for an aggressive pipeline backend.

Instruction fetch behavior has been shown to be very regular and predictable, even for diverse application areas. In this work, we propose the Lookahead Instruction Fetch Engine (LIFE), which is designed to exploit the regularity present in instruction fetch. The nucleus of LIFE is the Tagless Hit Instruction Cache (TH-IC), a small cache that assists the instruction fetch pipeline stage as it efficiently captures information about both sequential and non-sequential transitions between instructions. TH-IC provides a considerable savings in fetch energy without incurring the performance penalty normally associated with small filter instruction caches. LIFE extends TH-IC by making use of advanced control flow metadata to further improve utilization of fetch-associated structures such as the branch predictor, branch target buffer, and return address stack. These structures are selectively disabled by LIFE when it can be determined that they are unnecessary for the following instruction to be fetched. Our results show that LIFE enables further reductions in total processor energy consumption with no impact on application execution times even for the most aggressive power-saving configuration. We also explore the use of LIFE metadata on guiding decisions further down the pipeline. Next sequential line prefetch for the data cache can be enhanced by only prefetching when the triggering instruction has been previously accessed in the TH-IC. This strategy reduces the number of useless prefetches and thus contributes to improving overall processor efficiency. LIFE enables designers to boost instruction fetch efficiency by reducing energy cost without negatively affecting performance.

Energy consumption plays a crucial role in the design of embedded processors especially for portable devices. Since memory access consumes a significant portion of the energy of a processor, the design of fast low-energy caches has become a very important aspect of modern processor design. In this paper, we present a novel cache architecture for reduced energy instruction caches. Our proposed cache architecture consists of the L1 cache, multiple line buffers, and a prediction mechanism to predict which line buffer, or L1 cache to access next. We used simulation to evaluate our proposed architecture and compare it with the HotSpot cache, Filter cache, Predictive line buffer cache and Way-Halting cache. Simulation results show that our approach can reduce instruction cache energy consumption, on average, by 75% without sacrificing performance.