Register renaming is a technique which is used to improve the performance and speed for a high-performance processors. It can be done using RAM , CAM and hybrid combination of RAM & CAM.
RAR (Read After Read) is not considered a data hazard because it does not change the order of memory accesses or introduce incorrect results. Multiple instructions can safely read the same register without interfering with each other. The three types of data hazards that can occur are RAW (Read After Write), WAR (Write After Read), and WAW (Write After Write) which all involve write operations that could potentially overwrite data before it is read.
DESIGN AND ANALYSIS OF A 32-BIT PIPELINED MIPS RISC PROCESSORVLSICS Design
The document describes the design and analysis of a 32-bit pipelined MIPS RISC processor. A 6-stage pipeline is implemented, consisting of instruction fetch, instruction decode, register read, memory access, execute, and write back stages. Various techniques are used to optimize critical performance factors like power, frequency, area, and propagation delay. Power gating is applied to minimize power consumption, and deeper pipelining is used to increase speed. Simulation results show the pipeline consumes very low power of 0.129W, has a path delay of 11.180ns, and achieves a high frequency of 285.583MHz.
This document discusses instruction-level parallelism (ILP) limitations. It covers ILP background using a MIPS example, hardware models that were studied including register renaming and branch/jump prediction assumptions. A study of ILP limitations found diminishing returns with larger window sizes and realizable processors are limited by complexity and power constraints. Simultaneous multithreading was explored as a technique to improve ILP but has its own design challenges. Today, x86 and ARM processors employ various ILP optimizations within pipeline constraints.
Vector processing involves executing the same operation on multiple data elements simultaneously using a single instruction. Early implementations like the CDC Cyber 100 had limitations. The Cray-1 was the first successful vector processing supercomputer, using vector registers to perform calculations faster than requiring memory access. Seymour Cray led the development of vector processing machines that dominated the field for many years. While vector processing is no longer a focus, its principles are still used today in multimedia SIMD instructions.
Pipelining is a technique used in microprocessors where the execution of an instruction is broken down into stages that can be executed concurrently for different instructions. This allows a new instruction to begin executing before the previous one has finished. The document provides an example of a four-stage integer arithmetic pipeline and calculates the speedup from pipelining over a non-pipelined approach for 100 tasks. It is explained that pipelining can reduce the execution time from 8,000 ns to 2,060 ns, providing a speedup of 3.88x.
This document discusses instruction-level parallelism (ILP), which refers to executing multiple instructions simultaneously in a program. It describes different types of parallel instructions that do not depend on each other, such as at the bit, instruction, loop, and thread levels. The document provides an example to illustrate ILP and explains that compilers and processors aim to maximize ILP. It outlines several ILP techniques used in microarchitecture, including instruction pipelining, superscalar, out-of-order execution, register renaming, speculative execution, and branch prediction. Pipelining and superscalar processing are explained in more detail.
RAR (Read After Read) is not considered a data hazard because it does not change the order of memory accesses or introduce incorrect results. Multiple instructions can safely read the same register without interfering with each other. The three types of data hazards that can occur are RAW (Read After Write), WAR (Write After Read), and WAW (Write After Write) which all involve write operations that could potentially overwrite data before it is read.
DESIGN AND ANALYSIS OF A 32-BIT PIPELINED MIPS RISC PROCESSORVLSICS Design
The document describes the design and analysis of a 32-bit pipelined MIPS RISC processor. A 6-stage pipeline is implemented, consisting of instruction fetch, instruction decode, register read, memory access, execute, and write back stages. Various techniques are used to optimize critical performance factors like power, frequency, area, and propagation delay. Power gating is applied to minimize power consumption, and deeper pipelining is used to increase speed. Simulation results show the pipeline consumes very low power of 0.129W, has a path delay of 11.180ns, and achieves a high frequency of 285.583MHz.
This document discusses instruction-level parallelism (ILP) limitations. It covers ILP background using a MIPS example, hardware models that were studied including register renaming and branch/jump prediction assumptions. A study of ILP limitations found diminishing returns with larger window sizes and realizable processors are limited by complexity and power constraints. Simultaneous multithreading was explored as a technique to improve ILP but has its own design challenges. Today, x86 and ARM processors employ various ILP optimizations within pipeline constraints.
Vector processing involves executing the same operation on multiple data elements simultaneously using a single instruction. Early implementations like the CDC Cyber 100 had limitations. The Cray-1 was the first successful vector processing supercomputer, using vector registers to perform calculations faster than requiring memory access. Seymour Cray led the development of vector processing machines that dominated the field for many years. While vector processing is no longer a focus, its principles are still used today in multimedia SIMD instructions.
Pipelining is a technique used in microprocessors where the execution of an instruction is broken down into stages that can be executed concurrently for different instructions. This allows a new instruction to begin executing before the previous one has finished. The document provides an example of a four-stage integer arithmetic pipeline and calculates the speedup from pipelining over a non-pipelined approach for 100 tasks. It is explained that pipelining can reduce the execution time from 8,000 ns to 2,060 ns, providing a speedup of 3.88x.
This document discusses instruction-level parallelism (ILP), which refers to executing multiple instructions simultaneously in a program. It describes different types of parallel instructions that do not depend on each other, such as at the bit, instruction, loop, and thread levels. The document provides an example to illustrate ILP and explains that compilers and processors aim to maximize ILP. It outlines several ILP techniques used in microarchitecture, including instruction pipelining, superscalar, out-of-order execution, register renaming, speculative execution, and branch prediction. Pipelining and superscalar processing are explained in more detail.
The document discusses pipelining in computer processors. It explains that pipelining allows for overlapping execution of multiple instructions to improve processor throughput. An analogy is drawn to an assembly line in laundry - non-pipelined execution is like completing an entire load sequentially, while pipelined is like having different stages of multiple loads occurring in parallel. Pipelining is achieved by breaking instruction execution into discrete stages, such as fetch, decode, execute, memory, and writeback. This allows new instructions to enter the pipeline before previous ones have finished, improving instruction completion rate.
The document discusses the structure and function of the central processing unit (CPU). It covers the following key points in 3 sentences:
The CPU must fetch, decode, and process instructions, fetching any required data. It uses registers for temporary storage and processing, including general purpose, data, address, and condition code registers. Different CPU designs vary in the number and functions of registers, which are the top level of the memory hierarchy.
The document discusses computer architecture and describes the seven dimensions of an Instruction Set Architecture (ISA). It also defines dependability and its two measures - reliability and availability. Some example performance measurements are provided along with the processor performance equation. Finally, it discusses measuring, reporting, and summarizing computer performance using benchmarks and benchmark suites.
Parallel processing involves performing multiple tasks simultaneously to increase computational speed. It can be achieved through pipelining, where instructions are overlapped in execution, or vector/array processors where the same operation is performed on multiple data elements at once. The main types are SIMD (single instruction multiple data) and MIMD (multiple instruction multiple data). Pipelining provides higher throughput by keeping the pipeline full but requires handling dependencies between instructions to avoid hazards slowing things down.
Pipelining is a technique used in modern processors to improve performance. It allows multiple instructions to be processed simultaneously using different processor components. This increases throughput compared to sequential processing. However, pipeline stalls can occur due to data hazards when instructions depend on each other, instruction hazards from branches or cache misses, or structural hazards when resources are needed simultaneously. Various techniques like forwarding, reordering, and branch prediction aim to reduce the impact of hazards on pipeline performance.
This document discusses parallel processing techniques such as pipelining and vector processing to increase computational speed. It covers Flynn's classification of computer architectures, arithmetic pipelining using a floating-point adder as an example, instruction pipelining with a four-segment model, resolving data dependencies and branch difficulties in pipelines, and RISC pipeline examples addressing delayed load and branch issues. The key techniques discussed are decomposing operations into parallel suboperations, hardware interlocks, operand forwarding, and compiler assistance.
The document discusses pipelining in computer processors. It describes how pipelining can increase throughput by overlapping the execution of multiple instructions. It discusses the basic pipeline stages for a RISC instruction set, including fetch, decode, execute, memory access, and writeback. It also describes several types of pipeline hazards that can occur, such as structural hazards caused by resource conflicts, data hazards when instructions depend on previous results, and control hazards with branches. Forwarding techniques are presented to help address data hazards.
The document discusses instruction pipelining in processors. It explains that instruction pipelining allows consecutive instructions to be fetched from memory while previous instructions are being executed in different pipeline stages. It identifies some challenges in instruction pipelining including different stage times, dependencies between instructions, and branch instructions. It proposes techniques to address these challenges such as combining pipeline stages, rearranging instructions, forwarding operands, and branch prediction.
pipelining is the concept of decomposing the sequential process into number of small stages in which each stage execute individual parts of instruction life cycle inside the processor.
The document discusses parallelism and techniques to improve computer performance through parallel execution. It describes instruction level parallelism (ILP) where multiple instructions can be executed simultaneously through techniques like pipelining and superscalar processing. It also discusses processor level parallelism using multiple processors or processor cores to concurrently execute different tasks or threads.
This document discusses loop parallelization and pipelining as well as trends in parallel systems and forms of parallelism. It describes loop transformations like permutation, reversal, and skewing that can be used to parallelize loops. It also discusses parallelization conditions, wavefront transformations for fine-grained parallelism, and tiling to improve data locality. The document then covers software pipelining of loops to reduce execution time. Finally, it discusses trends in parallel computing and different forms of parallelism like instruction-level, data, and task parallelism.
This document discusses instruction pipelining as a technique to improve computer performance. It explains that pipelining allows multiple instructions to be processed simultaneously by splitting instruction execution into stages like fetch, decode, execute, and write. While pipelining does not reduce the time to complete individual instructions, it improves throughput by allowing new instructions to begin processing before previous instructions have finished. The document outlines some challenges to achieving peak performance from pipelining, such as pipeline stalls from hazards like data dependencies between instructions. It provides examples of how data hazards can occur if the results of one instruction are needed by a subsequent instruction before they are available.
The document analyzes the performance of the LEON 3FT processor at different operating frequencies. A hardware implementation using the LEON 3FT processor was tested by executing benchmark programs at various frequencies. The results show that execution time decreases with higher operating frequencies, though there is a maximum frequency limit due to hardware constraints. Future work involves attempting to increase this maximum frequency limit while maintaining processor performance.
The document discusses instruction pipelining in CPUs. It explains that instruction pipelining achieves greater CPU performance by overlapping the execution of multiple instructions. It describes the different stages in a basic two-stage pipeline as fetch and execute. It then discusses how further dividing the pipeline into more stages, such as six stages for fetch, decode, calculate, fetch operands, execute, and writeback, can provide even higher performance. However, it notes conditional branches can reduce efficiency since the next instruction is unknown until the branch is resolved. Various techniques to handle branches like branch prediction, prefetching the target, and delayed branches are described to improve pipeline performance.
Pipelining is an implementation technique where multiple instructions are overlapped in execution. The computer pipeline is divided in stages. Each stage completes a part of an instruction in parallel.
High performance pipelined architecture of elliptic curve scalar multiplicati...Ieee Xpert
High performance pipelined architecture of elliptic curve scalar multiplication over gf(2m) High performance pipelined architecture of elliptic curve scalar multiplication over gf(2m) High performance pipelined architecture of elliptic curve scalar multiplication over gf(2m) High performance pipelined architecture of elliptic curve scalar multiplication over gf(2m)
This document describes a system for reconfiguring memory to enable high-speed matrix multiplication. The system uses three RAMs (MAT-RAM-A, MAT-RAM-B, MAT-RAM-C) and a control circuit. It operates in two modes: in mode 1 the RAMs act as extensions of the processor's memory, and in mode 2 the RAMs are configured for hardware matrix multiplication with MAT-RAM-A and MAT-RAM-B as inputs and MAT-RAM-C as the output storage. The RAMs are then reconfigured to mode 1 to read the output from MAT-RAM-C. This approach reduces the time for matrix multiplication compared to traditional methods.
This document describes a system for reconfiguring memory to enable high-speed matrix multiplication. The system uses three RAMs (MAT-RAM-A, MAT-RAM-B, MAT-RAM-C) that can be configured in two modes: mode 1 where they act as extensions of the processor's memory, and mode 2 where MAT-RAM-A and MAT-RAM-B store input matrices and MAT-RAM-C stores the output matrix. In mode 2, an external control unit performs the multiplication and stores results directly in MAT-RAM-C, bypassing the processor to greatly increase speed compared to traditional methods. The RAMs are then switched back to mode 1 so results can be accessed normally.
The document discusses pipelining in computer processors. It explains that pipelining allows for overlapping execution of multiple instructions to improve processor throughput. An analogy is drawn to an assembly line in laundry - non-pipelined execution is like completing an entire load sequentially, while pipelined is like having different stages of multiple loads occurring in parallel. Pipelining is achieved by breaking instruction execution into discrete stages, such as fetch, decode, execute, memory, and writeback. This allows new instructions to enter the pipeline before previous ones have finished, improving instruction completion rate.
The document discusses the structure and function of the central processing unit (CPU). It covers the following key points in 3 sentences:
The CPU must fetch, decode, and process instructions, fetching any required data. It uses registers for temporary storage and processing, including general purpose, data, address, and condition code registers. Different CPU designs vary in the number and functions of registers, which are the top level of the memory hierarchy.
The document discusses computer architecture and describes the seven dimensions of an Instruction Set Architecture (ISA). It also defines dependability and its two measures - reliability and availability. Some example performance measurements are provided along with the processor performance equation. Finally, it discusses measuring, reporting, and summarizing computer performance using benchmarks and benchmark suites.
Parallel processing involves performing multiple tasks simultaneously to increase computational speed. It can be achieved through pipelining, where instructions are overlapped in execution, or vector/array processors where the same operation is performed on multiple data elements at once. The main types are SIMD (single instruction multiple data) and MIMD (multiple instruction multiple data). Pipelining provides higher throughput by keeping the pipeline full but requires handling dependencies between instructions to avoid hazards slowing things down.
Pipelining is a technique used in modern processors to improve performance. It allows multiple instructions to be processed simultaneously using different processor components. This increases throughput compared to sequential processing. However, pipeline stalls can occur due to data hazards when instructions depend on each other, instruction hazards from branches or cache misses, or structural hazards when resources are needed simultaneously. Various techniques like forwarding, reordering, and branch prediction aim to reduce the impact of hazards on pipeline performance.
This document discusses parallel processing techniques such as pipelining and vector processing to increase computational speed. It covers Flynn's classification of computer architectures, arithmetic pipelining using a floating-point adder as an example, instruction pipelining with a four-segment model, resolving data dependencies and branch difficulties in pipelines, and RISC pipeline examples addressing delayed load and branch issues. The key techniques discussed are decomposing operations into parallel suboperations, hardware interlocks, operand forwarding, and compiler assistance.
The document discusses pipelining in computer processors. It describes how pipelining can increase throughput by overlapping the execution of multiple instructions. It discusses the basic pipeline stages for a RISC instruction set, including fetch, decode, execute, memory access, and writeback. It also describes several types of pipeline hazards that can occur, such as structural hazards caused by resource conflicts, data hazards when instructions depend on previous results, and control hazards with branches. Forwarding techniques are presented to help address data hazards.
The document discusses instruction pipelining in processors. It explains that instruction pipelining allows consecutive instructions to be fetched from memory while previous instructions are being executed in different pipeline stages. It identifies some challenges in instruction pipelining including different stage times, dependencies between instructions, and branch instructions. It proposes techniques to address these challenges such as combining pipeline stages, rearranging instructions, forwarding operands, and branch prediction.
pipelining is the concept of decomposing the sequential process into number of small stages in which each stage execute individual parts of instruction life cycle inside the processor.
The document discusses parallelism and techniques to improve computer performance through parallel execution. It describes instruction level parallelism (ILP) where multiple instructions can be executed simultaneously through techniques like pipelining and superscalar processing. It also discusses processor level parallelism using multiple processors or processor cores to concurrently execute different tasks or threads.
This document discusses loop parallelization and pipelining as well as trends in parallel systems and forms of parallelism. It describes loop transformations like permutation, reversal, and skewing that can be used to parallelize loops. It also discusses parallelization conditions, wavefront transformations for fine-grained parallelism, and tiling to improve data locality. The document then covers software pipelining of loops to reduce execution time. Finally, it discusses trends in parallel computing and different forms of parallelism like instruction-level, data, and task parallelism.
This document discusses instruction pipelining as a technique to improve computer performance. It explains that pipelining allows multiple instructions to be processed simultaneously by splitting instruction execution into stages like fetch, decode, execute, and write. While pipelining does not reduce the time to complete individual instructions, it improves throughput by allowing new instructions to begin processing before previous instructions have finished. The document outlines some challenges to achieving peak performance from pipelining, such as pipeline stalls from hazards like data dependencies between instructions. It provides examples of how data hazards can occur if the results of one instruction are needed by a subsequent instruction before they are available.
The document analyzes the performance of the LEON 3FT processor at different operating frequencies. A hardware implementation using the LEON 3FT processor was tested by executing benchmark programs at various frequencies. The results show that execution time decreases with higher operating frequencies, though there is a maximum frequency limit due to hardware constraints. Future work involves attempting to increase this maximum frequency limit while maintaining processor performance.
The document discusses instruction pipelining in CPUs. It explains that instruction pipelining achieves greater CPU performance by overlapping the execution of multiple instructions. It describes the different stages in a basic two-stage pipeline as fetch and execute. It then discusses how further dividing the pipeline into more stages, such as six stages for fetch, decode, calculate, fetch operands, execute, and writeback, can provide even higher performance. However, it notes conditional branches can reduce efficiency since the next instruction is unknown until the branch is resolved. Various techniques to handle branches like branch prediction, prefetching the target, and delayed branches are described to improve pipeline performance.
Pipelining is an implementation technique where multiple instructions are overlapped in execution. The computer pipeline is divided in stages. Each stage completes a part of an instruction in parallel.
High performance pipelined architecture of elliptic curve scalar multiplicati...Ieee Xpert
High performance pipelined architecture of elliptic curve scalar multiplication over gf(2m) High performance pipelined architecture of elliptic curve scalar multiplication over gf(2m) High performance pipelined architecture of elliptic curve scalar multiplication over gf(2m) High performance pipelined architecture of elliptic curve scalar multiplication over gf(2m)
This document describes a system for reconfiguring memory to enable high-speed matrix multiplication. The system uses three RAMs (MAT-RAM-A, MAT-RAM-B, MAT-RAM-C) and a control circuit. It operates in two modes: in mode 1 the RAMs act as extensions of the processor's memory, and in mode 2 the RAMs are configured for hardware matrix multiplication with MAT-RAM-A and MAT-RAM-B as inputs and MAT-RAM-C as the output storage. The RAMs are then reconfigured to mode 1 to read the output from MAT-RAM-C. This approach reduces the time for matrix multiplication compared to traditional methods.
This document describes a system for reconfiguring memory to enable high-speed matrix multiplication. The system uses three RAMs (MAT-RAM-A, MAT-RAM-B, MAT-RAM-C) that can be configured in two modes: mode 1 where they act as extensions of the processor's memory, and mode 2 where MAT-RAM-A and MAT-RAM-B store input matrices and MAT-RAM-C stores the output matrix. In mode 2, an external control unit performs the multiplication and stores results directly in MAT-RAM-C, bypassing the processor to greatly increase speed compared to traditional methods. The RAMs are then switched back to mode 1 so results can be accessed normally.
FPGA Implementation of High Speed AMBA Bus Architecture for Image Transmissio...IRJET Journal
This document proposes modifying the standard AMBA bus architecture to support simultaneous read and write operations to memory, which is needed for some applications but not supported in the original AMBA design. It describes designing a high-speed AMBA bus architecture for image transmission and face detection using FPGA. The proposed design uses a finite state machine-based memory controller and pipeline data transfer to reduce wait states and improve system performance compared to existing AMBA designs. It aims to implement this modified AMBA architecture on FPGA for real-time image processing applications.
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Investigations on Implementation of Ternary Content Addressable Memory Archit...IRJET Journal
This document discusses investigations on implementing a ternary content addressable memory (TCAM) architecture called Z-TCAM in a Spartan 3E field programmable gate array (FPGA). The Z-TCAM architecture was implemented and achieved a hardware utilization of 12.26%, latency of 3110.55 nanoseconds, and power consumption of 45.16 milliwatts. Previous TCAM implementations and architectures are also reviewed, including issues with TCAM density, speed, and power consumption compared to static random access memory (SRAM) technologies. The document focuses on optimizing TCAM architectures for factors like area, power, latency, and capacity.
This document presents a new technique to reduce power consumption and increase speed in Content Addressable Memory (CAM) using memory partition and clock gating. The proposed CAM design partitions the memory into segments based on the most significant bits to check for matches. Since most words fail to match in their segments, the search can be discontinued for those segments, reducing power and increasing speed. Clock gating is also used to power gate unused portions of the CAM, further reducing static and dynamic power. Simulation and analysis using Quartus II and ModelSim show the proposed design reduces total power dissipation from 369.9mW to 46.3mW compared to an existing design.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
The document discusses several topics related to computer architecture:
1. It compares DRAM and SRAM, noting that DRAM is slower but has higher storage capacity than SRAM.
2. It defines cache coherence as maintaining consistent data across multiple local caches.
3. A microprocessor incorporates all central processing functions on a single chip and uses microprograms to provide control logic for the CPU.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
The document summarizes an HDL implementation of an AMBA-AHB compatible memory controller. Key points:
1) A memory controller was designed that is compliant with the Advanced Microcontroller Bus Architecture (AMBA) and interfaces as an Advanced High-performance Bus (AHB) slave.
2) The memory controller supports multiple memory devices like SRAM and ROM. It complies with the AHB protocol and supports 1-4 memory banks.
3) The architecture of the AHB memory controller consists of an AHB slave interface, configuration interface, and external memory interface. It uses asynchronous FIFOs between clock domains and burst transfers are supported to improve performance.
4) The
JPM1402 An Efficient Parallel Approach for Sclera Vein Recognitionchennaijp
This document proposes a new parallel approach for sclera vein recognition to improve matching efficiency. Existing CPU-based methods are slow, taking 1.5 seconds on average for one-to-one matching. The proposed method uses a two-stage coarse-to-fine matching approach on GPUs. It extracts rotation and scale invariant Y-shape descriptors to eliminate unlikely matches. It also uses a weighted polar line descriptor and mapping scheme to reduce GPU memory usage and allow parallel processing. Experimental results show the new approach achieves significant speed improvements without reducing recognition accuracy.
1. The document discusses the design of a low-cost multiprocessing platform for packet processing using network processors. It analyzes the performance bottlenecks of traditional architectures and proposes an optimized instruction set.
2. The analysis profiles common routing algorithms to identify key instructions affecting performance. Load, move, and shift instructions were found to greatly impact speed.
3. Based on this, the authors developed a multiprocessing platform on an FPGA with four simple RISC cores and an optimized instruction set. This bottom-up approach aims to optimize memory usage and lower clock speeds for reduced power consumption.
Power minimization of systems using Performance Enhancement Guaranteed CachesIJTET Journal
Caches have long been an instrument for speeding memory access from microcontrollers to center based ASIC plans. For hard ongoing frameworks however stores are tricky because of most pessimistic scenario execution time estimation. As of late, an on-chip scratch cushion memory (SPM) to decrease the force and enhance execution. SPM does not productively reuse its space while execution. Here, an execution improvement ensured reserves (PEG-C) to improve the execution. It can likewise be utilized like a standard reserve to progressively store guidelines and information in view of their runtime access examples prompting attain to great execution. All the earlier plans have corruption of execution when contrasted with PEG-C. It has a superior answer for equalization time consistency and normal case execution
The document discusses the Chameleon Chip, a reconfigurable processor that can rewire itself dynamically to adapt to different software tasks. It contains reconfigurable processing fabric divided into slices that can be reconfigured independently. Algorithms are loaded sequentially onto the fabric for high performance. The chip architecture includes an ARC processor, memory controller, PCI controller, and programmable I/O. Its applications include wireless base stations, wireless local loops, and software-defined radio.
International Journal of Engineering Research and DevelopmentIJERD Editor
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Marine and Agriculture engineering,
Aerospace Engineering.
The document discusses fragmentation issues that arise from data deduplication in backup storage systems. It proposes three algorithms - History-Aware Rewriting algorithm (HAR), Cache-Aware Filter (CAF), and Container-Marker Algorithm (CMA) - to address these issues. Experimental results on real-world datasets show that HAR can significantly improve restore performance by 2.84-175.36 times while only rewriting 0.5-2.03% of data.
The document discusses several machine learning projects at NECST Research. It summarizes projects involving behavior identification in animals using models like XGBoost, muscle synergy identification using NMF and neural networks on FPGA, deep learning acceleration on embedded devices using HLS, spiking neural networks for robot simulation, CNN acceleration on FPGA using CONDOR, and the PRETZEL system for optimizing multiple similar ML models deployed on cloud platforms.
This document describes research on an efficient reconfigurable content addressable memory (CAM). CAM is a type of memory that can perform high-speed searches. It introduces a cache-CAM (C-CAM) that adds a small cache memory to reduce the high power consumption of CAMs. Simulation and test chip results show the C-CAM can save 40-80% power compared to a conventional CAM by caching frequently accessed data and avoiding searches of the larger CAM in many cases. C-CAM performance depends on the cache size and hit rate, with the maximum power savings achieved with a cache size of around 4K bits and a hit rate of 90%.
The document discusses the performance implications of two types of processing-in-memory (PIM) designs - fixed-functional PIM and programmable PIM - on data-intensive applications. It explores these implications through three benchmarks, including a real data analytics application involving gradient computation. The results show that PIMs provide speedups ranging from 2.09x to 91.4x over non-PIM designs. However, fixed-functional and programmable PIMs perform differently across applications, with up to a 90% performance difference. Neither PIM type is optimal for all cases. The best choice depends on workload and PIM characteristics as well as PIM overhead.
Similar to Efficient register renaming and recovery for high-performance processors. (20)
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4. Modern superscalar microprocessors implement
out-of-order and speculative execution.
Register renaming technique is used increase the
performance of the processor.
Modern superscalar processors implement
register renaming using either RAM or CAM.
4
5. 5
Register renaming uses two kinds of registers-
Logical registers
Physical registers
A new hybrid scheme is presented here.
It combines the best of both RAM-CAM register
renaming approaches.
6. Many mechanisms are there for enhancing the
concurrent execution of instructions.
All these mechanisms require register renaming
technique.
Register renaming techniques solve write after read
and write after write data hazards.
RAM and CAM are two approaches used for register
renaming at present.
6
7. 7
RAM Approach
RAMs provide faster access times.
RAM provides energy efficient access to register
mappings.
It’s not appropriate to avoid recovery penalties.
RAM approaches use a free register queue(FRQ)
8. 8
CAM Approach
CAM structures have as many rows as the
number of available physical registers.
Each row maintains the information for
renaming and recovery.
It is more appropriate to avoid recovery
penalties.
Source registers are renamed to physical
registers.
9. HYBRID RAM–CAM approach for Register Renaming
Hybrid RAM-CAM approach is a combination of
both RAM and CAM register renaming.
The hybrid scheme uses two methods:
A CAM containing all register mappings up to
date
A RAM acting as a cache of the CAM
9
10. 10
The CAM is indexed by physical registers or
logical registers.
The RAM is indexed by a logical registers.
Register renaming is performed by just accessing
the RAM.
In hybrid RAM–CAM.
(a) Instruction reach at the rename stage.
(b) Destination registers are mapped to the new
physical registers
(c)The new mapping is recorded both in the
RAM and the CAM.
12. 12
The block diagram can be divided in three
sections:
A.Clearing Previous Destination Mappings
B. Destination Register Renaming
C. Source Register Renaming
13. 13
A. Clearing Previous Destination Mappings
CAM entries corresponding to previous
destination mappings are cleared
For the valid entries, physical register identifiers
are obtained
B. Destination Register Renaming
Free physical registers are mapped to the
destination registers.
Setting and updating new mappings.
14. 14
C. Source Register Renaming
The RAM is accessed in the first stage.
Mappings are obtained.
On a miss, an associative CAM search is
performed in the second stage.
A hazard arises during the second stage.
15. Fast and energy-efficient access to register mappings.
Reduces the power consumption.
It reduces the access time.
Leakage energy is lower for hybrid schemes
15
17. This technique can be used in instruction
level processors(ILP)
To remove false data dependencies in a
straight code sequence
17
18. In processors where high speed and energy-
efficient register renaming is required.
18
19. Presented a renaming mechanism consisting of a
RAM and a CAM
A final hybrid design that took the best of both
approaches
Hybrid designs also reduced the dynamic energy
Hybrid designs are to be more efficient than the
RAM approaches both in terms area and energy
19
20. [1] Efficient Register Renaming and Recovery
for High-Performance Processors by
Salvador Petit, Member, IEEE, Rafael Ubal, Julio
Sahuquillo, Member, IEEE, and Pedro López,
Member, IEEE
20