Digital processing-in-memory (PIM) architectures mitigate the memory wall problem by facilitating parallel bitwise operations directly within the memory. Recent works have demonstrated their algorithmic potential for accelerating data-intensive applications; however, there remains a significant gap in the programming model and microarchitectural design. This is further exacerbated by aspects unique to memristive PIM such as partitions and operations across both directions of the memory array. To address this gap, this paper provides an end-to-end architectural integration of digital memristive PIM from a high-level Python library for tensor operations (similar to NumPy and PyTorch) to the low-level microarchitectural design. We begin by proposing an efficient microarchitecture and instruction set architecture (ISA) that bridge the gap between the low-level control periphery and an abstraction of PIM parallelism. We subsequently propose a PIM development library that converts high-level Python to ISA instructions and a PIM driver that translates ISA instructions into PIM micro-operations. We evaluate PyPIM via a cycle-accurate simulator on a wide variety of benchmarks that both demonstrate the versatility of the Python library and the performance compared to theoretical PIM bounds. Overall, PyPIM drastically simplifies the development of PIM applications and enables the conversion of existing tensor-oriented Python programs to PIM with ease.

Processing-in-memory (PIM) architectures allow software to explicitly initiate computation in the memory. This effectively makes PIM operations a new class of memory operations, alongside standard memory operations (e.g., load, store). For software correctness, it is crucial to have ordering rules for a PIM operation with other PIM operations and other memory operations, i.e., a consistency model that takes into account PIM operations is vital. To the best of our knowledge, little attention to PIM operation consistency has been given in existing works. In this paper, we focus on a specific PIM approach, named bulk-bitwise PIM. In bulk-bitwise PIM, large bitwise operations are performed directly and stored in the memory array. We show that previous solutions for the related topic of maintaining coherency of bulk-bitwise PIM have broken the host native consistency model and prevent any guaranteed correctness. As a solution, we propose and evaluate four consistency models for bulk-bitwise PIM, from strict to relaxed. Our designs also preserve coherency between PIM and the host processor. Evaluating the proposed designs’ performance with a gem5 simulation, using the YCSB short-range scan benchmark and TPC-H queries, shows that the run time overhead of guaranteeing correctness is at most 6%, and in many cases the run time is even improved. The hardware overhead of our design is less than 0.22%.

The emerging memristive Memory Processing Unit (mMPU) overcomes the memory wall through memristive devices that unite storage and logic for real processing-in-memory (PIM) systems. At the core of the mMPU is stateful logic, which is accelerated with memristive partitions to enable logic with massive inherent parallelism within crossbar arrays. This paper vastly accelerates the fundamental operations of matrix-vector multiplication and convolution in the mMPU, with either full-precision or binary elements. These proposed algorithms establish an efficient foundation for large-scale mMPU applications such as neural-networks, image processing, and numerical methods. We overcome the inherent asymmetry limitation in the previous in-memory full-precision matrix-vector multiplication solutions by utilizing techniques from block matrix multiplication and reduction. We present the first fast in-memory binary matrix-vector multiplication algorithm by utilizing memristive partitions with a tree-based popcount reduction (39$\times$ faster than previous work). For convolution, we present a novel in-memory input-parallel concept which we utilize for a full-precision algorithm that overcomes the asymmetry limitation in convolution, while also improving latency (2$\times$ faster than previous work), and the first fast binary algorithm (12$\times$ faster than previous work).

Currently, data-intensive applications are gaining popularity. Together with this trend, processing-in-memory (PIM)–based systems are being given more attention and have become more relevant. This article describes an analytical modeling tool called Bitlet that can be used in a parameterized fashion to estimate the performance and power/energy of a PIM-based system and, thereby, assess the affinity of workloads for PIM as opposed to traditional computing. The tool uncovers interesting trade-offs between, mainly, the PIM computation complexity (cycles required to perform a computation through PIM), the amount of memory used for PIM, the system memory bandwidth, and the data transfer size. Despite its simplicity, the model reveals new insights when applied to real-life examples. The model is demonstrated for several synthetic examples and then applied to explore the influence of different parameters on two systems — IMAGING and FloatPIM. Based on the demonstrations, insights about PIM and its combination with a CPU are provided.

Inefficient data transfer between computation and memory inspired emerging processing-in-memory (PIM) technologies. Many PIM solutions enable storage and processing using memristors in a crossbar-array structure, with techniques such as memristor-aided logic (MAGIC) used for computation. This approach provides highly-paralleled logic computation with minimal data movement. However, memristors are vulnerable to soft errors and standard error-correcting-code (ECC) techniques are difficult to implement without moving data outside the memory. We propose a novel technique for efficient ECC implementation along diagonals to support reliable computation inside the memory without explicitly reading the data. Our evaluation demonstrates an improvement of over eight orders of magnitude in reliability (mean time to failure) for an increase of about 26% in computation latency.

Computationally intensive neural network applications often need to run on resource-limited low-power devices. Numerous hardware accelerators have been developed to speed up the performance of neural network applications and reduce power consumption; however, most focus on data centers and full-fledged systems. Acceleration in ultra-low-power systems has been only partially addressed. In this article, we present multiPULPly, an accelerator that integrates memristive technologies within standard low-power CMOS technology, to accelerate multiplication in neural network inference on ultra-low-power systems. This accelerator was designated for PULP, an open-source microcontroller system that uses low-power RISC-V processors. Memristors were integrated into the accelerator to enable power consumption only when the memory is active, to continue the task with no context-restoring overhead, and to enable highly parallel analog multiplication. To reduce the energy consumption, we propose novel dataflows that handle common multiplication scenarios and are tailored for our architecture. The accelerator was tested on FPGA and achieved a peak energy efficiency of 19.5 TOPS/W, outperforming state-of-the-art accelerators by 1.5× to 4.5×.

Data-intensive applications are poised to benefit directly from processing-in-memory platforms, such as memristive Memory Processing Units, which allow leveraging data locality and performing stateful logic operations. Developing design automation flows for such platforms is a challenging and highly relevant research problem. In this work, we investigate the problem of minimizing delay under arbitrary area constraint for MAGIC-based in-memory computing platforms. We propose an end-to-end area constrained technology mapping framework, CONTRA. CONTRA uses Look-Up Table (LUT) based mapping of the input function on the crossbar array to maximize parallel operations and uses a novel search technique to move data optimally inside the array. CONTRA supports benchmarks in a variety of formats, along with crossbar dimensions as input to generate MAGIC instructions. CONTRA scales for large benchmarks, as demonstrated by our experiments. CONTRA allows mapping benchmarks to smaller crossbar dimensions than achieved by any other technique before, while allowing a wide variety of area-delay trade-offs. CONTRA improves the composite metric of area-delay product by 2.1× to 13.1× compared to seven existing technology mapping approaches.

The von Neumann architecture, in which the memory and the computation units are separated, demands massive data traffic between the memory and the CPU. To reduce data movement, new technologies and computer architectures have been explored. The use of memristors, which are devices with both memory and computation capabilities, has been considered for different processing-in-memory (PIM) solutions, including using memristive stateful logic for a programmable digital PIM system. Nevertheless, all previous work has focused on a specific stateful logic family, and on optimizing the execution for a certain target machine. These solutions require new compiler and compilation when changing the target machine, and provide no backward compatibility with other target machines. In this paper, we present abstractPIM, a new compilation concept and flow which enables executing any function within the memory, using different stateful logic families and different instruction set architectures (ISAs). By separating the code generation into two independent components, intermediate representation of the code using target independent ISA and then microcode generation for a specific target machine, we provide a flexible flow with backward compatibility and lay foundations for a PIM compiler. Using abstractPIM, we explore various logic technologies and ISAs and how they impact each other, and discuss the challenges associated with it, such as the increase in execution time.

Processing-in-memory (PIM) using memristive technologies is an attractive solution for the memory wall problem. PIM can improve the performance and energy efficiency of computing systems by reducing the data transfer between the memory and the processor. Memristor Aided loGIC (MAGIC) is a popular memristive PIM technique that can perform any combinational logic as a sequence of atomic NOR/NOT operations. These NOR/NOT operations rely on initializing their output cell prior to computation. In this paper, we explore input overwriting: the use of the MAGIC gate output cell as an additional input without initializing it. We extend MAGIC and introduce X-MAGIC (eXtended MAGIC) which uses input overwriting, and demonstrate it by two gates, A·(B+C) and A.B̅, where A is an overwritten input. We show that input overwriting improves functionality, performance, and effective lifetime of the system. Due to algorithmic difficulties, available PIM synthesis tools do not support input overwriting. We address these difficulties by modifying an existing synthesis tool for MAGIC (SIMPLER), and presenting several general principles and methods for supporting input overwriting. We examine two configurations of the modified synthesis tool using X-MAGIC gates, differing in their performance/area trade-off. Both configurations achieve a geomean improvement of over 16.5% in performance, and over 20% in effective lifetime compared to standard MAGIC. Due to algorithmic difficulties, available PIM synthesis tools do not support input overwriting. We address these difficulties by modifying an existing synthesis tool for MAGIC (SIMPLER), and presenting several general principles and methods for supporting input overwriting. We examine two configurations of the modified synthesis tool using X-MAGIC gates, differing in their performance/area trade-off. Both configurations achieve a geomean improvement of over 16.5% in performance, and over 20% in effective lifetime compared to standard MAGIC.

In-memory processing can dramatically improve the latency and energy consumption of computing systems by minimizing the data transfer between the memory and the processor. Efficient execution of processing operations within the memory is therefore, a highly motivated objective in modern computer architecture. This article presents a novel automatic framework for efficient implementation of arbitrary combinational logic functions within a memristive memory. Using tools from logic design, graph theory and compiler register allocation technology, we developed synthesis and in-memory mapping of logic execution in a single row (SIMPLER), a tool that optimizes the execution of in-memory logic operations in terms of throughput and area. Given a logical function, SIMPLER automatically generates a sequence of atomic memristor-aided logic (MAGIC) NOR operations and efficiently locates them within a single sizelimited memory row, reusing cells to save area when needed. This approach fully exploits the parallelism offered by the MAGIC NOR gates. It allows multiple instances of the logic function to be performed concurrently, each compressed into a single row of the memory. This virtue makes SIMPLER an attractive candidate for designing in-memory single instruction, multiple data (SIMD) operations. Compared to the previous work (that optimizes latency rather than throughput for a single function), SIMPLER achieves an average throughput improvement of 435×. When the previous tools are parallelized similarly to SIMPLER, SIMPLER achieves higher throughput of at least 5×, with 23× improvement in area and 20× improvement in area efficiency. These improvements more than fully compensate for the increase (up to 17% on average) in latency.

Processing-in-Memory (PiM) machines promise to overcome the von Neumann bottleneck in order to further scale performance and energy efficiency of computing systems by reducing the extent of data transfer and offering ample parallelism. In this paper, we take the memristive Memory Processing Unit (mMPU) as a case study of a PiM machine and scrutinize it in practical scenarios. Specifically, we explore the limitations of parallelism and data transfer elimination. We argue that lack of operand locality and arrangement might make data transfer inevitable in the mMPU. We then devise techniques to move data within the mMPU, without transferring it off-chip, and quantify their costs. Additionally, we present electrical parameters that might limit the parallelism offered by the mMPU and evaluate their impact. Using benchmarks from the LGsynth91 suite, their vector extensions, and a few synthetic data-parallel workloads, we show that the internal data transfer results in an increase of up to 1.5× in the execution time, while the parallelism can be limited in some cases to 256 gates, resulting in an increase in execution time by 1.1× to 2×.

Traditionally, architectural innovations designed to boost single-threaded performance incur overhead costs which significantly increase power consumption. In many cases the increase in power exceeds the improvement in performance, resulting in a net increase in energy consumption. Thus, it is reasonable to assume that modern attempts to improve single-threaded performance will have a negative impact on energy efficiency. This has led to the belief that “Big Cores” are inherently inefficient. To the contrary, we present a study which finds that the increased complexity of the core microarchitecture in recent generations of the Intel®Core™ processor have reduced both the time and energy required to run various workloads. Moreover, taking out the impact of process technology changes, our study still finds the architecture and microarchitecture changes -such as the increase in SIMD width, addition of the frontend caches, and the enhancement to the out-of-order execution engine- account for 1.2x improvement in energy efficiency for these processors. This paper provides real-world examples of how architectural innovations can mitigate inefficiencies associated with “Big Cores” -for example, micro-op caches obviate the costly decode of complex x86 instructions- resulting in a core architecture that is both high performance and energy efficient. It also contributes to the understanding of how microarchitecture affects performance, power and energy efficiency by modeling the relationship between them.

Small scale chip multiprocessors are being shipped in volume by all microprocessor vendors. Many of these vendors are also investigating large scale chip multiprocessors targeted towards highly parallel workloads in media, graphics, and others. One of the most challenging aspects of architecting terascale processors is the design of a scalable memory hierarchy. Current proposals for providing coherent shared memory in terascale systems require a sophisticated coherence protocol and memory hierarchy. In this paper we propose an alternate memory configuration along with a programming model that significantly simplifies the terascale memory hierarchy. Our proposal still provides fully coherent shared memory but eliminates the hardware coherence protocol. Our programming model enables the programmer to better express the memory characteristic of terascale workloads. Finally, our proposed memory hierarchy performs better and is more scalable than conventional designs.

The client computing platform is moving towards a heterogeneous architecture consisting of a combination of cores focused on scalar performance, and a set of throughput-oriented cores. The throughput oriented cores (e.g. a GPU) may be connected over both coherent and non-coherent interconnects, and have different ISAs. This paper describes a programming model for such heterogeneous platforms. We discuss the language constructs, runtime implementation, and the memory model for such a programming environment. We implemented this programming environment in a x86 heterogeneous platform simulator. We ported a number of workloads to our programming environment, and present the performance of our programming environment on these workloads.

This paper describes the tradeoff between latency performance and throughput performance in a power-constrained environment. We show that the key to achieving both excellent latency performance as well as excellent throughput performance is to dynamically vary the amount of energy expended to process instructions according to the amount of parallelism available in the software. We survey four techniques for achieving variable energy per instruction: voltage/frequency scaling, asymmetric cores, variable-size cores, and speculation control. We estimate the potential range of energies obtainable by each technique and conclude that a combination of asymmetric cores and voltage/frequency scaling offers the most promising approach to design a chip-level multiprocessor that can achieve both excellent latency performance and excellent throughput performance.

This paper performs a comprehensive investigation of dynamic selection for long atomic traces. It introduces a classification of trace selection methods and discusses existing and novel dynamic selection approaches - including loop unrolling, procedure in-lining and incremental merging of traces based on dynamic bias. The paper empirically analyzes a number of selection schemes in an idealized framework.Observations based on the SPEC-CPU2000 benchmarks show that: (a) selection based on dynamic bias is necessary to achieve the best performance across all benchmarks, (b) the best selection scheme is benchmark and maximum trace-length specific, (c) simple selection, based on program structure information only, is sufficient to achieve the best performance for several benchmarks.Consequently, two alternatives for the trace selection mechanism are established: (a) a "best performance" approach relying on complex dynamic criteria; (b) a "value" approach that provides the best performance (and potentially the best power consumption) based on simpler static criteria. Another emerging alternative advocates adaptive based mechanisms to adjust selection criteria.

Modern computer architectures that support variable length instruction set architectures (ISA), such as the Intel's IA-32, distinguish between the architectural level of presentation and the micro-architectural representations of the instructions. At the micro-architectural level, instructions are represented by fixed-length micro-operations termed uops, and complex instructions are broken into sequence of uops. The fetch and decode operations in such architectures are extremely complicated and power hungry, especially if they aim to handle several variable length instructions per cycle. This paper suggests caching uop sequences from decoded instructions in a special structure, termed uop cache (UC), and use this fix-length decoded format when possible. Doing so enables reduction in the processor's power and energy consumption while not compromising performance. We will show that a moderately-sized UC can eliminate about 75% instruction decodes across a broad range of benchmarks and over 90% in multimedia applications and high-power tests. For existing Intel P6 family processors, the eliminated work may save about 10% of the full-chip power consumption. While the new proposed technique can be used to save power without degrading performance, we can also use it to improve processor performance when power is constrained.

Article Share on Micro-operation cache: a power aware frontend for the variable instruction length ISA Authors: Baruch Solomon Intel Corporation, Intel Israel (74) Ltd., P.O. Box 1659, Haifa 31015, Israel Intel Corporation, Intel Israel (74) Ltd., P.O. Box 1659, Haifa 31015, IsraelView Profile , Avi Mendelson Intel Corporation, Intel Israel (74) Ltd., P.O. Box 1659, Haifa 31015, Israel Intel Corporation, Intel Israel (74) Ltd., P.O. Box 1659, Haifa 31015, IsraelView Profile , Doron Orenstein Intel Corporation, Intel Israel (74) Ltd., P.O. Box 1659, Haifa 31015, Israel Intel Corporation, Intel Israel (74) Ltd., P.O. Box 1659, Haifa 31015, IsraelView Profile , Yoav Almog Intel Corporation, Intel Israel (74) Ltd., P.O. Box 1659, Haifa 31015, Israel Intel Corporation, Intel Israel (74) Ltd., P.O. Box 1659, Haifa 31015, IsraelView Profile , Ronny Ronen Intel Corporation, Intel Israel (74) Ltd., P.O. Box 1659, Haifa 31015, Israel Intel Corporation, Intel Israel (74) Ltd., P.O. Box 1659, Haifa 31015, IsraelView Profile Authors Info & Claims ISLPED '01: Proceedings of the 2001 international symposium on Low power electronics and designAugust 2001 Pages 4–9https://doi.org/10.1145/383082.383085Published:06 August 2001Publication History 22citation475DownloadsMetricsTotal Citations22Total Downloads475Last 12 Months85Last 6 weeks22 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

In the past several decades, the world of computers and especially that of microprocessors has witnessed phenomenal advances. Computers have exhibited ever-increasing performance and decreasing costs, making them more affordable and in turn, accelerating additional software and hardware development that fueled this process even more. The technology that enabled this exponential growth is a combination of advancements in process technology, microarchitecture, architecture, and design and development tools. While the pace of this progress has been quite impressive over the last two decades, it has become harder and harder to keep up this pace. New process technology requires more expensive megafabs and new performance levels require larger die, higher power consumption, and enormous design and validation effort. Furthermore, as CMOS technology continues to advance, microprocessor design is exposed to a new set of challenges. In the near future, microarchitecture has to consider and explicitly manage the limits of semiconductor technology, such as wire delays, power dissipation, and soft errors. In this paper we describe the role of microarchitecture in the computer world present the challenges ahead of us, and highlight areas where microarchitecture can help address these challenges.

Modern microprocessors employ dynamic instruction scheduling to select independent instructions for parallel execution. Good scheduling of loads is crucial, since the long latency of some loads makes them likely to degrade performance. A good scheduler attempts to issue loads as early as possible. Scheduling loads is not simple. First, safely resolving a load's input dependences can be done only at execution time, after the load address and all previous store addresses are known. Second, varying load latency makes it difficult to prioritize loads and to efficiently schedule load-dependent instructions. This paper surveys several techniques that optimize load scheduling. Memory disambiguation resolves store-load dependences and enables earlier execution of store-independent loads. Memory renaming and memory bypassing short-circuit memory to streamline the passing of values from stores to loads. Critical path scheduling, pre-execution, and address prediction advance long-latency loads by computing load addresses early, or predicting them. Value prediction short-circuits load execution by predicting the loaded data values. Finally, data speculation and hit-miss prediction help the scheduling of load-dependent instructions.

This paper describes a new instruction-supply mechanism, called the eXtended Block Cache (XBC). The goal of the XBC is to improve on the Trace Cache (TC) hit rate, while providing the same bandwidth. The improved hit rate is achieved by having the XBC a nearly redundant free structure. The basic unit recorded in the XBC is the extended block (XB), which is a multiple-entry single-exit instruction block. A XB is a sequence of instructions ending on a conditional or an indirect branch. Unconditional direct jumps do not end a XB. In order to enable multiple entry points per XB, the XB index is derived from the IP of its ending instruction. Instructions within the XB are recorded in reverse order, enabling easy extension of XBs. The multiple entry-points remove most of the redundancy. Since there is at most one conditional branch per XB, we can fetch up to n XBs per cycle by predicting n branches. The multiple fetch enables the XBC to march the TC bandwidth.

Higher microprocessor frequencies accentuate the performance cost of memory accesses. This is especially noticeable in the Intel's IA32 architecture where lack of registers results in increased number of memory accesses. This paper presents novel, non-speculative technique that partially hides the increasing load-to-use latency, by allowing the early issue of load instructions. Early load address resolution relies on register tracking to safely compute the addresses of memory references in the front-end part of the processor pipeline. Register tracking may be performed in any pipeline stage following instruction decode and prior to execution. Several tracking schemes are proposed in this paper: Stack pointer tracking allows safe early resolution of stack references by keeping track of the value of the ESP register (the stack pointer). About 25% of all loads are stack loads and 95% of these lends may be resolved in the front-end. Absolute address tracking allows the early resolution of constant-address loads. Displacement-based tracking tackles all loads with addresses of the form reg+immediate by tracking the values of all general-purpose registers. This class corresponds to 82% of all loads, and about 65% of these loads can be safely resolved in the front-end pipeline. The paper describes the tracking schemes, analyzes their performance potential in a deeply pipelined processor and discusses the integration of tracking with memory disambiguation.

As microprocessors become faster, the relative performance cost of memory accesses increases. Bigger and faster caches significantly reduce the absolute load-to-use time delay. However, increase in processor operational frequencies impairs the relative load-to-use latency, measured in processor cycles (e.g. from two cycles on the Pentium processor to three cycles of more in current designs). Load-address prediction techniques were introduced to partially cut the load-to-use latency. This paper focuses on advanced address-prediction schemes to further shorten program execution time. Existing address prediction schemes are capable of predicting simple address patterns, consisting mainly of constant addresses or stride-based addresses. This paper explores the characteristics of the remaining loads and suggests new enhanced techniques to improve prediction effectiveness: Context-based prediction to tackle part of the remaining, difficult-to-predict, load instructions. New prediction algorithms to take advantage of global correlation among different static loads. New confidence mechanisms to increase the correct prediction rate and to eliminate costly mispredictions. Mechanisms to prevent long or random address sequences from polluting the predictor data structures while providing some hysteresis behavior to the predictions. Such an enhanced address predictor accurately predicts 67% of all loads, while keeping the misprediction rate close to 1% We further prove that the proposed predictor works reasonably well in a deep pipelined architecture where the predict-to-update delay may significantly impair both prediction rate and accuracy.

State of the art microprocessors achieve high performance by executing multiple instructions per cycle. In an out-of-order engine, the instruction scheduler is responsible for dispatching instructions to execution units based on dependencies, latencies, and resource availability. Most existing instruction schedulers are doing a less than optimal job of scheduling memory accesses and instructions dependent on them, for the following reasons: Memory dependencies cannot be resolved prior to execution, so loads are not advanced ahead of preceding stores. The dynamic latencies of load instructions are unknown, so scheduling dependent instructions is based on either optimistic load-use delay (may cause re-scheduling and re-execution) or pessimistic delay (creating unnecessary delays). Memory pipelines are more expensive than other execution units, and as such, are a scarce resource. Currently, an increase in the memory execution bandwidth is usually achieved through multi-banked caches where bank conflicts limit efficiency. In this paper we present three techniques to address these scheduler limitations. One is to improve the scheduling of load instructions by using a simple memory disambiguation mechanism. The second is to improve the scheduling of load dependent instructions by employing a Data Cache Hit-Miss Predictor to predict the dynamic load latencies. And the third is to improve the efficiency of load scheduling in a multi-banked cache through cache-bank prediction.

Hardware renaming schemes provide multiple physical locations (register or memory) for each logical name. In current renaming schemes, a new physical location is allocated for each dispatched instruction regardless of its result value. However, these values exhibit a high level of temporal locality (result redundancy). This paper proposes: Physical Register Reuse. To reuse a physical location whenever it is detected that an incoming result value matches a previous one. This is performed during register renaming and requires some VALUE-IDENTITY DETECTION hardware. By mapping several logical registers holding the same value to the same physical register, Physical Register Reuse gives the opportunities: SHARING-exploit the high level of value-redundancy in the register file to either reduce the file size and complexity, or effectively enlarge the active instruction window. Our results suggest reduction factors of 2 to 4 in some cases. Performance is increased either by the enlarged instruction window or by the higher frequency enabled by a smaller register file requiring fewer ports. RESULT REUSE AND DEPENDENCY REDIRECTION-move the responsibility of generating results: (1) From the functional units to the register renamer, resulting in the possible elimination of processed instructions from the execution stream. (2) From one instruction to an earlier instruction stream, possibly allowing instructions to be scheduled earlier. This way, large performance speedups are achieved. 2. Unification. To combine the memory renamer with the register renamer in order to extend the above-stated sharing and result reuse and dependency redirection ideas to both registers and memory locations. This allows even greater hardware savings and performance improvements. This also simplifies the processing of store instructions.