Accelerating Falcon on ARMv8
Falcon is one of the promising digital-signature algorithms in NIST's ongoing Post-Quantum Cryptography (PQC) standardization finalist. Computational efficiency regarding software and hardware is also the main criteria for PQC standardization. In this paper, we present an efficient Falcon softw...
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description | Falcon is one of the promising digital-signature algorithms in NIST's ongoing Post-Quantum Cryptography (PQC) standardization finalist. Computational efficiency regarding software and hardware is also the main criteria for PQC standardization. In this paper, we present an efficient Falcon software implementation on ARMv8 environment. Until now, most of the software optimization on PQC algorithms have been conducted on 32-bit ARM (Cortex-M4) and typical CPUs (Intel and AMD CPUs). However, ARMv8 including Cortex-A30, 50, and 70 series have been widely used for various IoT (Internet of Things) applications, Edge computing devices, and OBUs (On Board Units) in autonomous driving cars. For optimizing the performance of Falcon, we take full advantage of NEON engine which is a kind of parallel processing unit in ARMv8 MCU. The main computation in Falcon belongs to polynomial multiplications in Complex number domain and Integer domain. Typically, FFT (Fast Fourier Transformation)-based multiplication method and NTT (Number Theoriteic Transform)-based multiplication method have been widely used for efficient polynomial multiplications in Complex number domain and Integer domain, respectively. Thus, in order to enhance the overall performance of Falcon, we improve the FFT-based multiplication method and NTT-based multiplication method by utilizing NEON engine in ARMv8. Specifically, we parallelize the overall process (FFT/NTT transformation, pointwise multiplication, and inverse FFT/NTT transformation) of FFT-based polynomial multiplication method and NTT-based polynomial multiplication method with strategically utilizing the NEON engine and vector instructions. Furthermore, we minimize the number of redundant memory accesses during FFT/NTT-based polynomial multiplication by making the most of available registers in NEON engine. Through the proposed parallel FFT/NTT-based polynomial multiplications, the proposed Falcon software provides 15.1% (resp. 18.1%), 16.5% (resp. 17.1%), and 65.4% (resp. 69.4%) of performance improvement in keypair generation, signing, and verification at security level 1 (resp. 5) compared with the reference Falcon implementation submitted to the final round of NIST PQC competition. Furthermore, as far as we know, this is the first optimized implementation of Falcon on ARMv8 environment. |
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Computational efficiency regarding software and hardware is also the main criteria for PQC standardization. In this paper, we present an efficient Falcon software implementation on ARMv8 environment. Until now, most of the software optimization on PQC algorithms have been conducted on 32-bit ARM (Cortex-M4) and typical CPUs (Intel and AMD CPUs). However, ARMv8 including Cortex-A30, 50, and 70 series have been widely used for various IoT (Internet of Things) applications, Edge computing devices, and OBUs (On Board Units) in autonomous driving cars. For optimizing the performance of Falcon, we take full advantage of NEON engine which is a kind of parallel processing unit in ARMv8 MCU. The main computation in Falcon belongs to polynomial multiplications in Complex number domain and Integer domain. Typically, FFT (Fast Fourier Transformation)-based multiplication method and NTT (Number Theoriteic Transform)-based multiplication method have been widely used for efficient polynomial multiplications in Complex number domain and Integer domain, respectively. Thus, in order to enhance the overall performance of Falcon, we improve the FFT-based multiplication method and NTT-based multiplication method by utilizing NEON engine in ARMv8. Specifically, we parallelize the overall process (FFT/NTT transformation, pointwise multiplication, and inverse FFT/NTT transformation) of FFT-based polynomial multiplication method and NTT-based polynomial multiplication method with strategically utilizing the NEON engine and vector instructions. Furthermore, we minimize the number of redundant memory accesses during FFT/NTT-based polynomial multiplication by making the most of available registers in NEON engine. Through the proposed parallel FFT/NTT-based polynomial multiplications, the proposed Falcon software provides 15.1% (resp. 18.1%), 16.5% (resp. 17.1%), and 65.4% (resp. 69.4%) of performance improvement in keypair generation, signing, and verification at security level 1 (resp. 5) compared with the reference Falcon implementation submitted to the final round of NIST PQC competition. Furthermore, as far as we know, this is the first optimized implementation of Falcon on ARMv8 environment.</description><identifier>ISSN: 2169-3536</identifier><identifier>EISSN: 2169-3536</identifier><identifier>DOI: 10.1109/ACCESS.2022.3169784</identifier><identifier>CODEN: IAECCG</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Addition & subtraction ; Algorithms ; ARM/NEON processor ; ARMv8 ; Central processing units ; Complex numbers ; CPUs ; Cryptography ; Digital signatures ; Domains ; Edge computing ; Engines ; Falcon ; Fast Fourier transformations ; Integers ; Internet of Things ; memory optimization ; Multiplication ; Neon ; NIST PQC signature ; Optimization ; parallel implementation ; Parallel processing ; Polynomials ; Quantum cryptography ; Security ; Software ; Software algorithms ; Standardization</subject><ispartof>IEEE access, 2022, Vol.10, p.44446-44460</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c408t-685749385a0e952ef669df55fcfcc7ccd87d97304f2f43b3c6cd40f1779c406a3</citedby><cites>FETCH-LOGICAL-c408t-685749385a0e952ef669df55fcfcc7ccd87d97304f2f43b3c6cd40f1779c406a3</cites><orcidid>0000-0001-8016-2808 ; 0000-0003-4715-8393</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9762260$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,864,2102,4024,27633,27923,27924,27925,54933</link.rule.ids></links><search><creatorcontrib>Kim, Youngbeom</creatorcontrib><creatorcontrib>Song, Jingyo</creatorcontrib><creatorcontrib>Seo, Seog Chung</creatorcontrib><title>Accelerating Falcon on ARMv8</title><title>IEEE access</title><addtitle>Access</addtitle><description>Falcon is one of the promising digital-signature algorithms in NIST's ongoing Post-Quantum Cryptography (PQC) standardization finalist. Computational efficiency regarding software and hardware is also the main criteria for PQC standardization. In this paper, we present an efficient Falcon software implementation on ARMv8 environment. Until now, most of the software optimization on PQC algorithms have been conducted on 32-bit ARM (Cortex-M4) and typical CPUs (Intel and AMD CPUs). However, ARMv8 including Cortex-A30, 50, and 70 series have been widely used for various IoT (Internet of Things) applications, Edge computing devices, and OBUs (On Board Units) in autonomous driving cars. For optimizing the performance of Falcon, we take full advantage of NEON engine which is a kind of parallel processing unit in ARMv8 MCU. The main computation in Falcon belongs to polynomial multiplications in Complex number domain and Integer domain. Typically, FFT (Fast Fourier Transformation)-based multiplication method and NTT (Number Theoriteic Transform)-based multiplication method have been widely used for efficient polynomial multiplications in Complex number domain and Integer domain, respectively. Thus, in order to enhance the overall performance of Falcon, we improve the FFT-based multiplication method and NTT-based multiplication method by utilizing NEON engine in ARMv8. Specifically, we parallelize the overall process (FFT/NTT transformation, pointwise multiplication, and inverse FFT/NTT transformation) of FFT-based polynomial multiplication method and NTT-based polynomial multiplication method with strategically utilizing the NEON engine and vector instructions. Furthermore, we minimize the number of redundant memory accesses during FFT/NTT-based polynomial multiplication by making the most of available registers in NEON engine. Through the proposed parallel FFT/NTT-based polynomial multiplications, the proposed Falcon software provides 15.1% (resp. 18.1%), 16.5% (resp. 17.1%), and 65.4% (resp. 69.4%) of performance improvement in keypair generation, signing, and verification at security level 1 (resp. 5) compared with the reference Falcon implementation submitted to the final round of NIST PQC competition. Furthermore, as far as we know, this is the first optimized implementation of Falcon on ARMv8 environment.</description><subject>Addition & subtraction</subject><subject>Algorithms</subject><subject>ARM/NEON processor</subject><subject>ARMv8</subject><subject>Central processing units</subject><subject>Complex numbers</subject><subject>CPUs</subject><subject>Cryptography</subject><subject>Digital signatures</subject><subject>Domains</subject><subject>Edge computing</subject><subject>Engines</subject><subject>Falcon</subject><subject>Fast Fourier transformations</subject><subject>Integers</subject><subject>Internet of Things</subject><subject>memory optimization</subject><subject>Multiplication</subject><subject>Neon</subject><subject>NIST PQC signature</subject><subject>Optimization</subject><subject>parallel implementation</subject><subject>Parallel processing</subject><subject>Polynomials</subject><subject>Quantum cryptography</subject><subject>Security</subject><subject>Software</subject><subject>Software algorithms</subject><subject>Standardization</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNpNUE1LAzEQDaJgqf0F9VDwvGu-JzkuS6uFimD1HNJsUras3ZrdCv57U7cUZwbmMcx7MzyEpgTnhGD9WJTlfL3OKaY0Z0RqUPwKjWhCGRNMXv_Dt2jSdTucQqWRgBG6L5zzjY-2r_fb2cI2rt3PUhVvL9_qDt0E23R-cu5j9LGYv5fP2er1aVkWq8xxrPpMKgFcMyUs9lpQH6TUVRAiuOAcOFcpqDQwzAMNnG2Yk67iOBAAnQSkZWO0HHSr1u7MIdafNv6Y1tbmb9DGrbGxr13jDdcSNOdeA95waskpFXU0sApgAyxpPQxah9h-HX3Xm117jPv0vqFSaMaBaJW22LDlYtt10YfLVYLNyVUzuGpOrpqzq4k1HVi19_7C0CAplZj9Aoc4b1o</recordid><startdate>2022</startdate><enddate>2022</enddate><creator>Kim, Youngbeom</creator><creator>Song, Jingyo</creator><creator>Seo, Seog Chung</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>ESBDL</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7SR</scope><scope>8BQ</scope><scope>8FD</scope><scope>JG9</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0001-8016-2808</orcidid><orcidid>https://orcid.org/0000-0003-4715-8393</orcidid></search><sort><creationdate>2022</creationdate><title>Accelerating Falcon on ARMv8</title><author>Kim, Youngbeom ; Song, Jingyo ; Seo, Seog Chung</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c408t-685749385a0e952ef669df55fcfcc7ccd87d97304f2f43b3c6cd40f1779c406a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Addition & subtraction</topic><topic>Algorithms</topic><topic>ARM/NEON processor</topic><topic>ARMv8</topic><topic>Central processing units</topic><topic>Complex numbers</topic><topic>CPUs</topic><topic>Cryptography</topic><topic>Digital signatures</topic><topic>Domains</topic><topic>Edge computing</topic><topic>Engines</topic><topic>Falcon</topic><topic>Fast Fourier transformations</topic><topic>Integers</topic><topic>Internet of Things</topic><topic>memory optimization</topic><topic>Multiplication</topic><topic>Neon</topic><topic>NIST PQC signature</topic><topic>Optimization</topic><topic>parallel implementation</topic><topic>Parallel processing</topic><topic>Polynomials</topic><topic>Quantum cryptography</topic><topic>Security</topic><topic>Software</topic><topic>Software algorithms</topic><topic>Standardization</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kim, Youngbeom</creatorcontrib><creatorcontrib>Song, Jingyo</creatorcontrib><creatorcontrib>Seo, Seog Chung</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE Open Access Journals</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>IEEE access</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kim, Youngbeom</au><au>Song, Jingyo</au><au>Seo, Seog Chung</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Accelerating Falcon on ARMv8</atitle><jtitle>IEEE access</jtitle><stitle>Access</stitle><date>2022</date><risdate>2022</risdate><volume>10</volume><spage>44446</spage><epage>44460</epage><pages>44446-44460</pages><issn>2169-3536</issn><eissn>2169-3536</eissn><coden>IAECCG</coden><abstract>Falcon is one of the promising digital-signature algorithms in NIST's ongoing Post-Quantum Cryptography (PQC) standardization finalist. Computational efficiency regarding software and hardware is also the main criteria for PQC standardization. In this paper, we present an efficient Falcon software implementation on ARMv8 environment. Until now, most of the software optimization on PQC algorithms have been conducted on 32-bit ARM (Cortex-M4) and typical CPUs (Intel and AMD CPUs). However, ARMv8 including Cortex-A30, 50, and 70 series have been widely used for various IoT (Internet of Things) applications, Edge computing devices, and OBUs (On Board Units) in autonomous driving cars. For optimizing the performance of Falcon, we take full advantage of NEON engine which is a kind of parallel processing unit in ARMv8 MCU. The main computation in Falcon belongs to polynomial multiplications in Complex number domain and Integer domain. Typically, FFT (Fast Fourier Transformation)-based multiplication method and NTT (Number Theoriteic Transform)-based multiplication method have been widely used for efficient polynomial multiplications in Complex number domain and Integer domain, respectively. Thus, in order to enhance the overall performance of Falcon, we improve the FFT-based multiplication method and NTT-based multiplication method by utilizing NEON engine in ARMv8. Specifically, we parallelize the overall process (FFT/NTT transformation, pointwise multiplication, and inverse FFT/NTT transformation) of FFT-based polynomial multiplication method and NTT-based polynomial multiplication method with strategically utilizing the NEON engine and vector instructions. Furthermore, we minimize the number of redundant memory accesses during FFT/NTT-based polynomial multiplication by making the most of available registers in NEON engine. Through the proposed parallel FFT/NTT-based polynomial multiplications, the proposed Falcon software provides 15.1% (resp. 18.1%), 16.5% (resp. 17.1%), and 65.4% (resp. 69.4%) of performance improvement in keypair generation, signing, and verification at security level 1 (resp. 5) compared with the reference Falcon implementation submitted to the final round of NIST PQC competition. Furthermore, as far as we know, this is the first optimized implementation of Falcon on ARMv8 environment.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/ACCESS.2022.3169784</doi><tpages>15</tpages><orcidid>https://orcid.org/0000-0001-8016-2808</orcidid><orcidid>https://orcid.org/0000-0003-4715-8393</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Addition & subtraction Algorithms ARM/NEON processor ARMv8 Central processing units Complex numbers CPUs Cryptography Digital signatures Domains Edge computing Engines Falcon Fast Fourier transformations Integers Internet of Things memory optimization Multiplication Neon NIST PQC signature Optimization parallel implementation Parallel processing Polynomials Quantum cryptography Security Software Software algorithms Standardization |
title | Accelerating Falcon on ARMv8 |
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