VS-ISA: A Video Specific Instruction Set Architecture for ASIP Design

This paper analyses the performance of the state-of-the-art media ISA (instruction set architecture) extensions in a general-purpose processor, when executing a video encoder based on an affine motion model. In addition to SIMD (single instruction multiple data) fixed-point instructions, these ISA extensions include SIMD floating-point instructions, special-purpose SIMD fixed-point instructions, and cacheability control instructions. In this study, eight time-consuming kernels of the video encoder were hand-optimized, using instructions in all four instruction categories of these media ISA extensions (the FLP version). These kernels were also hand-optimized using only SIMD fixed-point ISA extensions, without special-purpose instructions (the FXP version). For the FLP version, this study resulted in an average kernel-level speedup of 1.37X and an application-level speedup of 1.11X, compared to the FXP version, and an application-level speedup of 3.41X, compared to the C version.

The emerging applications have imposed strong requirements, such as high processing capacity, low‐power consumption, reduced size and many others, on the sensor‐based systems. Owing to their balanced combination of flexibility and hardware performance, digital signal processors (DSPs) have become more and more popular used in sensor‐based systems. Many DSPs have adopted very long instruction words (VLIW) style architecture, for its ability to greatly enhance instruction level parallelism. However, as VLIW codes are statically scheduled, the behaviour of VLIW architecture is dominated by the efficiency of its compiler. Single instruction multiple data (SIMD) instructions, which perform multiple operations in parallel on multiple data packed in registers, have been widely used in DSPs to meet the requirements of sensor‐based systems. Although hand programming still yields the best performing SIMD codes, it is both time consuming and error prone. Advanced compiler techniques to automatically generate SIMD instructions, are under urgent demand. In this study, the authors proposed an SIMD code auto generation approach for VLIW architecture. It recognises candidates of operations in the intermediate representation, evaluates the possibility of grouping them into SIMD code, reconstructs the verified ones according to the cost model and finally generates the SIMD code. The authors have implemented this approach in the compiler of a VLIW DSP named Magnolia, which is designed for sensor‐based systems. The results show that the authors’ approach is very efficient, and can largely enhance the performance.

Software-defined radio (SDR) is a new technology transitioning from research into commercial markets. SDR moves hardware-dominant baseband processing of multiple wireless communication protocols into software on a single chip. New cellular standards, such as HSPA+, LTE, and LTE+, require speeds in excess of 40 Mbps. SNOW 3G is a new stream cipher approved for use in these cellular protocols. Running SNOW 3G in software on our SDR platform provides a throughput of 19.1 Mbps per thread for confidentiality and 18.3 Mbps per thread for integrity. To have secure cellular communications in SDR platforms for these new protocols, the performance of security algorithms must be improved. This paper presents instruction set architecture (ISA) extensions and hardware designs for cellular confidentiality and integrity algorithms using SNOW 3G. Our ISA extensions and hardware designs are evaluated for the Sandbridge Sandblaster? 3011 (SB3011) SDR platform. With our new SNOW 3G instructions, the performance of confidentiality and integrity improve by 70 and 2%, respectively. For confidentiality, power consumption increased by 2%, while energy decreased by 40%. For integrity, power consumption remained consistent, while energy decreased by 2%.

An embedded single media processor named MediaDSP3200 core fabricated in a six-layer metal 0.18um CMOS process which implemented the RISC instruction set, DSP data processing instruction set and single-instruction-multiple-data (SIMD) multimedia-enhanced instruction set is described. MediaDSP3200 fuses RISC architecture and DSP computation capability thoroughly, which achieves RISC fundamental, DSP extended and single instruction multiple data (SIMD) instruction set with various addressing modes in a unified pipeline stage architecture. These characteristics enhance system digital signal processing performance greatly. The test processor can achieve 32x32-bit multiply-accumulate (MAC) of 320 MOPS, with 16x16-bit MAC of 1280MOPS. The test processor dissipates 600mW at 1.8v, 320MHz. Also, the implementation was primarily standard cell logic design style. MediaDSP3200 targets diverse embedded application systems, which need both powerful processing/control capability and low-cost budget, e.g. set-top-boxes, video conferencing, DTV, etc. MediaDSP3200 instruction set architecture, addressing mode, pipeline design, SIMD feature, split-ALU and MAC are described in this paper. Finally, the performance benchmark based on H.264 and MPEG decoder algorithm are given in this paper.

This paper proposes a novel multi-core processor with SIMD(Single Instruction Multiple Data) ISA (Instruction Set Architecture) and extended register file for communication applications. To acquire better parallel computing capability, we implement SIMD ISA and increase the number of register file from 32 to 64. 5×5 homogeneous 2-D mesh NoC (Network-on-Chip) topology is adopted to further enhance the parallelism, scalability and programmability. RS (Reed-Solomon) (255,239,8) decoding algorithm is implemented to evaluate the performance. Simulation result shows it could achieve 2.175 Gbps of throughput in worst case of RS 8-error incoming codeword under a maximum 350MHz clock frequency at SMIC 0.13um worst process corner, and the throughput is higher than other published implementations.

This paper introduces an efficient technique to speed up the higher-order masking scheme of the advanced encryption standard (AES), in which the S-box computation is based on the composite field, using Intel's Supplemental Streaming SIMD Extensions 3 (SSSE 3) instruction set. Different side-channel attack (SCA) countermeasures are combined to mitigate more than one type of SCA. The proposed technique solves the problem of higher-order masking's negative impact on performance and removes the need for lookup tables by applying parallel processing at the data level using single instruction multiple data (SIMD) technology. We implemented the technique on a Linux machine with an Intel processor that supports SSSE3 instructions. The technique performed nine times faster than the second order masking scheme and was seven times faster than the third order masking scheme reported in the literature. The results that we achieved through applying SIMD technology make our proposed technique practical and attractive.

Managed language runtimes such as the Java Virtual Machine (JVM) provide adequate performance for a wide range of applications, but at the same time, they lack much of the low-level control that performance-minded programmers appreciate in languages like <pre>C/C++</pre>. One important example is the intrinsics interface that exposes instructions of SIMD (Single Instruction Multiple Data) vector ISAs (Instruction Set Architectures). In this paper we present an automatic approach for including native intrinsics in the runtime of a managed language. Our implementation consists of two parts. First, for each vector ISA, we automatically generate the intrinsics API from the vendor-provided XML specification. Second, we employ a metaprogramming approach that enables programmers to generate and load native code at runtime. In this setting, programmers can use the entire high-level language as a kind of macro system to define new high-level vector APIs with zero overhead. As an example use case we show a variable precision API. We provide an end-to-end implementation of our approach in the HotSpot VM that supports all 5912 Intel SIMD intrinsics from <pre>MMX</pre> to <pre>AVX-512</pre>. Our benchmarks demonstrate that this combination of SIMD and metaprogramming enables developers to write high-performance, vectorized code on an unmodified JVM that outperforms the auto-vectorizing HotSpot just-in-time (JIT) compiler and provides tight integration between vectorized native code and the managed JVM ecosystem.

This article discusses a MATLAB-to-C vectorizing compiler that exploits custom instructions, for example, for Single Instruction Multiple Data (SIMD) processing and instructions for complex arithmetic present in Application-Specific Instruction Set Processors (ASIPs). Custom instructions are represented via specialized intrinsic functions in the generated code, and the generated code can be used as input to any C/C++ compiler supporting the target processor. Furthermore, the specialized instruction set of the target processor is described in a parameterized way using a target processor-independent architecture description approach, thus allowing the support of any processor. The compiler has been used for the generation of application code for two different ASIPs for several benchmarks. The code generated by the compiler achieves a speedup between 2× --74× and 2× --97× compared to the code generated by the MathWorks MATLAB-to-C compiler. Experimental results also prove that the compiler efficiently exploits SIMD custom instructions achieving a 3.3 factor speedup compared to cases where no SIMD processing is used. Thus the compiler can be employed to reduce the development time/effort/cost and time to market through raising the abstraction of application design in an embedded systems/system-on-chip development context.

Vector instructions are ubiquitous in modern processors. Traditional compiler auto-vectorization techniques have focused on targeting single instruction multiple data (SIMD) instructions. However, these auto-vectorization techniques are not sufficiently powerful to model non-SIMD vector instructions, which can accelerate applications in domains such as image processing, digital signal processing, and machine learning. To target non-SIMD instruction, compiler developers have resorted to complicated, ad hoc peephole optimizations, expending significant development time while still coming up short. As vector instruction sets continue to rapidly evolve, compilers cannot keep up with these new hardware capabilities. In this paper, we introduce Lane Level Parallelism (LLP), which captures the model of parallelism implemented by both SIMD and non-SIMD vector instructions. We present VeGen, a vectorizer generator that automatically generates a vectorization pass to uncover target-architecture-specific LLP in programs while using only instruction semantics as input. VeGen decouples, yet coordinates automatically generated target-specific vectorization utilities with its target-independent vectorization algorithm. This design enables us to systematically target non-SIMD vector instructions that until now require ad hoc coordination between different compiler stages. We show that VeGen can use non-SIMD vector instructions effectively, for example, getting speedup 3× (compared to LLVM’s vectorizer) on x265’s idct4 kernel.

Use of single instruction multiple data (SIMD) functional units enables multimedia systems to exploit parallelism to a higher degree resulting in significant system performance improvements. While implementation of whole SIMD system functionality for an application results in wastage of area resources, we have observed that for a specific multimedia application, we only need to implement a customized SIMD unit that is a subset of whole SIMD standard implementation. Based on this study, we have proposed an extension to the traditional system design and synthesis flow by integrating a methodology of SIMD unit synthesis. Our system synthesizes a customized SIMD unit along with an extended instruction set and generates an equivalent version of assembly code for the application using the extended instruction set. The results of area and performance obtained by experimenting over our implementation of AltiVec compatible customized SIMD units show the effectiveness of our approach.

Heterogeneous processors that mix big high performance cores with small low power cores promise excellent single-threaded performance coupled with high multi-threaded throughput and higher performance-per-watt. A significant portion of the commercial multicore heterogeneous processors are likely to have a common instruction set architecture( ISA). However, due to limited design resources and goals, each core is likely to contain ISA extensions not yet implemented in the other core. Therefore, such heterogeneous processors will have inherent functional asymmetry at the ISA level and face significant software challenges. This paper analyzes the software challenges to the operating system and the application layer software on a heterogeneous system with functional asymmetry, where the ISA of the small and big cores overlaps. We look at the widely deployed Intel® Architecture and propose solutions to the software challenges that arise when a heterogeneous processor is designed around it. We broadly categorize functional asymmetries into those that can be exposed to application software and those that should be handled by system software. While one can argue that new software written should be heterogeneity-aware, it is important that we find ways in which legacy software can extract the best performance from heterogeneous multicore systems.

This paper presents a color signal processing technique for use in single-chip CCD cameras that have CPUs employing SIMD (single instruction multiple data) instruction sets for signal processing. With it, the processing time can be reduced to a half that of conventional techniques. Color space conversion is performed before color interpolation, rather than after, and as a vector operation; this reduces the number of required multiply-operations to 3 or 4, depending on the number of colors being used, as opposed to 9 for conventional 3 by 3 matrix processing.

On the use of a SIMD vector extension for the fast evaluation of Boundary Element Method coefficients

This thesis introduces and explores the design and verification of a packed Single Instruction, Multiple Data (SIMD) instruction set for a RISC-V processor, known as the RISC-V P extension. This extension enhances the RISC-V instruction set architecture by providing packed SIMD support for 8-bit, 16-bit, and 32-bit integer data types. The significance of this architecture lies in its potential to empower developers in constructing more efficient and powerful data-parallel programs for RISC-V processors. This contribution enhances the overall capabilities of the RISC-V ecosystem, providing a valuable extension to the instruction set architecture for data-parallel applications. Keywords: Data-parallel Programs, instruction set architecture, RISC-V, P extension, Packed SIMD

With the multitude of existing and upcoming wireless standards, it is becoming increasingly difficult for hardware-only baseband processing solutions to adapt to the rapidly changing wireless communication landscape. Software defined radio (SDR) promises to deliver a cost effective and flexible solution by implementing a wide variety of wireless protocols in software. In previous work, a fully programmable multicore architecture, SODA, was proposed that was able to meet the real-time requirements of 3G wireless protocols. SODA consists of one ARM control processor and four wide single instruction multiple data (SIMD) processing elements. Each processing element consists of a scalar and a wide 512-bit 32-lane SIMD datapath. A commercial prototype based on the SODA architecture, Ardbeg (named after a brand of Scotch whisky), has been developed. In this paper, we present the architectural evolution of going from a research design to a commercial prototype, including the goals, tradeoffs, and final design choices. Ardbegpsilas redesign process can be grouped into the following three major areas: optimizing the wide SIMD datapath, providing long instruction word (LIW) support for SIMD operations, and adding application-specific hardware accelerators. Because SODA was originally designed with 180 nm technology, the wide SIMD datapath is re-optimized in Ardbeg for 90 nm technology. This includes re-evaluating the most efficient SIMD width, designing a wider SIMD shuffle network, and implementing faster SIMD arithmetic units. Ardbeg also provides modest LIW support by allowing two SIMD operations to issue in the same cycle. This LIW execution supports SDR algorithmspsila most common parallel SIMD execution patterns with minimal hardware overhead. A viable commercial SDR solution must be competitive with existing ASIC solutions. Therefore, algorithm-specific hardware is added for performance bottleneck algorithms while still maintaining enough flexibility to support multiple wireless protocols. The combination of these architectural improvements allows Ardbeg to achieve 1.5-7x speedup over SODA across multiple wireless algorithms while consuming less power.