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• Cortex-A7 instruction cycle timings

  15 mai 2014, par Mans — ARM

  The Cortex-A7 ARM core is a popular choice in low-power and low-cost designs. Unfortunately, the public TRM does not include instruction timing information. It does reveal that execution is in-order which makes measuring the throughput and latency for individual instructions relatively straight-forward.

  The table below lists the measured issue cycles (inverse throughput) and result latency of some commonly used instructions.

  It should be noted that in some cases, the perceived latency depends on the instruction consuming the result. Most of the values were measured with the result used as input to the same instruction. For instructions with multiple outputs, the latencies of the result registers may also differ.

  Finally, although instruction issue is in-order, completion is out of order, allowing independent instructions to issue and complete unimpeded while a multi-cycle instruction is executing in another unit. For example, a 3-cycle MUL instruction does not block ADD instructions following it in program order.

  ALU instructions	Issue cycles	Result latency
  MOV Rd, Rm	1/2	1
  ADD Rd, Rn, #imm	1/2	1
  ADD Rd, Rn, Rm	1	1
  ADD Rd, Rn, Rm, LSL #imm	1	1
  ADD Rd, Rn, Rm, LSL Rs	1	1
  LSL Rd, Rn, #imm	1	2
  LSL Rd, Rn, Rs	1	2
  QADD Rd, Rn, Rm	1	2
  QADD8 Rd, Rn, Rm	1	2
  QADD16 Rd, Rn, Rm	1	2
  CLZ Rd, Rm	1	1
  RBIT Rd, Rm	1	2
  REV Rd, Rm	1	2
  SBFX Rd, Rn	1	2
  BFC Rd, #lsb, #width	1	2
  BFI Rd, Rn, #lsb, #width	1	2
  NOTE : Shifted operands and shift amounts needed one cycle early.
  Multiply instructions	Issue cycles	Result latency
  MUL Rd, Rn, Rm	1	3
  MLA Rd, Rn, Rm, Ra	1	31
  SMULL Rd, RdHi, Rn, Rm	1	3
  SMLAL Rd, RdHi, Rn, Rm	1	31
  SMMUL Rd, Rn, Rm	1	3
  SMMLA Rd, Rn, Rm, Ra	1	31
  SMULBB Rd, Rn, Rm	1	3
  SMLABB Rd, Rn, Rm, Ra	1	31
  SMULWB Rd, Rn, Rm	1	3
  SMLAWB Rd, Rn, Rm, Ra	1	31
  SMUAD Rd, Rn, Rm	1	3
  1 Accumulator forwarding allows back to back MLA instructions without delay.
  Divide instructions	Issue cycles	Result latency
  SDIV Rd, Rn, Rm	4-20	6-22
  UDIV Rd, Rn, Rm	3-19	5-21
  Load/store instructions	Issue cycles	Result latency
  LDR Rt, [Rn]	1	3
  LDR Rt, [Rn, #imm]	1	3
  LDR Rt, [Rn, Rm]	1	3
  LDR Rt, [Rn, Rm, lsl #imm]	1	3
  LDRD Rt, Rt2, [Rn]	1	3-4
  LDM Rn, regs	1-8	3-10
  STR Rt, [Rn]	1	2
  STRD Rt, Rt2, [Rn]	1	2
  STM Rn, regs	1-10	2-12
  NOTE : Load results are forwarded to dependent stores without delay.
  VFP instructions	Issue cycles	Result latency
  VMOV.F32 Sd, Sm	1	4
  VMOV.F64 Dd, Dm	1	4
  VNEG.F32 Sd, Sm	1	4
  VNEG.F64 Dd, Dm	1	4
  VABS.F32 Sd, Sm	1	4
  VABS.F64 Dd, Dm	1	4
  VADD.F32 Sd, Sn, Sm	1	4
  VADD.F64 Dd, Dn, Dm	1	4
  VMUL.F32 Sd, Sn, Sm	1	4
  VMUL.F64 Dd, Dn, Dm	4	7
  VMLA.F32 Sd, Sn, Sm	1	81
  VMLA.F64 Dd, Dn, Dm	4	112
  VFMA.F32 Sd, Sn, Sm	1	81
  VFMA.F64 Dd, Dn, Dm	5	82
  VDIV.F32 Sd, Sn, Sm	15	18
  VDIV.F64 Dd, Dn, Dm	29	32
  VSQRT.F32 Sd, Sm	14	17
  VSQRT.F64 Dd, Dm	28	31
  VCVT.F32.F64 Sd, Dm	1	4
  VCVT.F64.F32 Dd, Sm	1	4
  VCVT.F32.S32 Sd, Sm	1	4
  VCVT.F64.S32 Dd, Sm	1	4
  VCVT.S32.F32 Sd, Sm	1	4
  VCVT.S32.F64 Sd, Dm	1	4
  VCVT.F32.S32 Sd, Sd, #fbits	1	4
  VCVT.F64.S32 Dd, Dd, #fbits	1	4
  VCVT.S32.F32 Sd, Sd, #fbits	1	4
  VCVT.S32.F64 Dd, Dd, #fbits	1	4
  1 5 cycles with dependency only on accumulator. 2 8 cycles with dependency only on accumulator.
  NEON integer instructions	Issue cycles	Result latency
  VADD.I8 Dd, Dn, Dm	1	4
  VADDL.S8 Qd, Dn, Dm	2	4
  VADD.I8 Qd, Qn, Qm	2	4
  VMUL.I8 Dd, Dn, Dm	2	4
  VMULL.S8 Qd, Dn, Dm	2	4
  VMUL.I8 Qd, Qn, Qm	4	4
  VMLA.I8 Dd, Dn, Dm	2	4
  VMLAL.S8 Qd, Dn, Dm	2	4
  VMLA.I8 Qd, Qn, Qm	4	4
  VADD.I16 Dd, Dn, Dm	1	4
  VADDL.S16 Qd, Dn, Dm	2	4
  VADD.I16 Qd, Qn, Qm	2	4
  VMUL.I16 Dd, Dn, Dm	1	4
  VMULL.S16 Qd, Dn, Dm	2	4
  VMUL.I16 Qd, Qn, Qm	2	4
  VMLA.I16 Dd, Dn, Dm	1	4
  VMLAL.S16 Qd, Dn, Dm	2	4
  VMLA.I16 Qd, Qn, Qm	2	4
  VADD.I32 Dd, Dn, Dm	1	4
  VADDL.S32 Qd, Dn, Dm	2	4
  VADD.I32 Qd, Qn, Qm	2	4
  VMUL.I32 Dd, Dn, Dm	2	4
  VMULL.S32 Qd, Dn, Dm	2	4
  VMUL.I32 Qd, Qn, Qm	4	4
  VMLA.I32 Dd, Dn, Dm	2	4
  VMLAL.S32 Qd, Dn, Dm	2	4
  VMLA.I32 Qd, Qn, Qm	4	4
  NEON floating-point instructions	Issue cycles	Result latency
  VADD.F32 Dd, Dn, Dm	2	4
  VADD.F32 Qd, Qn, Qm	4	4
  VMUL.F32 Dd, Dn, Dm	2	4
  VMUL.F32 Qd, Qn, Qm	4	4
  VMLA.F32 Dd, Dn, Dm	2	81
  VMLA.F32 Qd, Qn, Qm	4	81
  1 5 cycles with dependency only on accumulator.
  NEON permute instructions	Issue cycles	Result latency
  VEXT.n Dd, Dn, Dm, #imm	1	4
  VEXT.n Qd, Qn, Qm, #imm	2	5
  VTRN.n Dd, Dn, Dm	2	5
  VTRN.n Qd, Qn, Qm	4	5
  VUZP.n Dd, Dn, Dm	2	5
  VUZP.n Qd, Qn, Qm	4	6
  VZIP.n Dd, Dn, Dm	2	5
  VZIP.n Qd, Qn, Qm	4	6
  VTBL.8 Dd, Dn, Dm	1	4
  VTBL.8 Dd, Dn-Dn+1, Dm	1	4
  VTBL.8 Dd, Dn-Dn+2, Dm	2	5
  VTBL.8 Dd, Dn-Dn+3, Dm	2	5

• How To Play Hardware Accelerated Video on A Mac

  28 mai 2013, par Multimedia Mike — General

  I have a friend who was considering purchasing a Mac Mini recently. At the time of this writing, there are 3 desktop models (and 2 more “server” models).

  
  
  

  The cheapest one is a Core i5 2.5 GHz. Then there are 2 Core i7 models : 2.3 GHz and 2.6 GHz. The difference between the latter 2 is US$100. The only appreciable technical difference is the extra 0.3 GHz and the choice came down to those 2.

  He asked me which one would be able to play HD video at full frame rate. I found this query puzzling. But then, I have been “in the biz” for a bit too long. Whether or not a computer or device can play a video well depends on a lot of factors.

  Hardware Support
  First of all, looking at the raw speed of the general-purpose CPU inside of a computer as a gauge of video playback performance is generally misguided in this day and age. In general, we have a video standard (H.264, which I’ll focus on for this post) and many bits of hardware are able to accelerate decoding. So, the question is not whether the CPU can decode the data in real time, but can any other hardware in the device (likely the graphics hardware) handle it ? These machines have Intel HD 4000 graphics and, per my reading of the literature, they are capable of accelerating H.264 video decoding.

  Great, so the hardware supports accelerated decoding. So it’s a done deal, right ? Not quite…

  Operating System Support
  An application can’t do anything pertaining to hardware without permission from the operating system. So the next question is : Does Mac OS X allow an application to access accelerated video decoding hardware if it’s available ? This used to be a contentious matter (notably, Adobe Flash Player was unable to accelerate H.264 playback on Mac in the absence of such an API) but then Apple released an official API detailed in Technical Note TN2267.

  So, does this mean that video is magically accelerated ? Nope, we’re still not there yet…

  Application Support
  It’s great that all of these underlying pieces are in place, but if an individual application chooses to decode the video directly on the CPU, it’s all for naught. An application needs to query the facilities and direct data through the API if it wants to leverage the acceleration. Obviously, at this point it becomes a matter of “which application ?”

  My friend eventually opted to get the pricier of the desktop Mac Mini models and we ran some ad-hoc tests since I was curious how widespread the acceleration support is among Mac multimedia players. Here are some programs I wanted to test, playing 1080p H.264 :

  • Apple QuickTime Player
  • VLC
  • YouTube with Flash Player (any browser)
  • YouTube with Safari/HTML5
  • YouTube with Chrome/HTML5
  • YouTube with Firefox/HTML5
  • Netflix

  I didn’t take exhaustive notes but my impromptu tests revealed QuickTime Player was, far and away, the most performant player, occupying only around 5% of the CPU according to the Mac OS X System Profiler graph (which is likely largely spent on audio decoding).

  VLC consistently required 20-30% CPU, so it’s probably leveraging some acceleration facilities. I think that Flash Player and the various HTML5 elements performed similarly (their multi-process architectures can make such a trivial profiling test difficult).

  The outlier was Netflix running in Firefox via Microsoft’s Silverlight plugin. Of course, the inner workings of Netflix’s technology are opaque to outsiders and we don’t even know if it uses H.264. It may very well use Microsoft’s VC-1 which is not a capability provided by the Mac OS X acceleration API (it doesn’t look like the Intel HD 4000 chip can handle it either). I have never seen any data one way or another about how Netflix encodes video. However, I was able to see that Netflix required an enormous amount of CPU muscle on the Mac platform.

  Conclusion
  The foregoing is a slight simplification of the video playback pipeline. There are some other considerations, most notably how the video is displayed afterwards. To circle back around to the original question : Can the Mac Mini handle full HD video playback ? As my friend found, the meager Mac Mini can do an admirable job at playing full HD video without loading down the CPU.

• What is Behavioural Segmentation and Why is it Important ?

  28 septembre 2023, par Erin — Analytics Tips

  Amidst the dynamic landscape of web analytics, understanding customers has grown increasingly vital for businesses to thrive. While traditional demographic-focused strategies possess merit, they need to uncover the nuanced intricacies of individual online behaviours and preferences. As customer expectations evolve in the digital realm, enterprises must recalibrate their approaches to remain relevant and cultivate enduring digital relationships.

  In this context, the surge of technology and advanced data analysis ushers in a marketing revolution : behavioural segmentation. Businesses can unearth invaluable insights by meticulously scrutinising user actions, preferences and online interactions. These insights lay the foundation for precisely honed, high-performing, personalised campaigns. The era dominated by blanket, catch-all marketing strategies is yielding to an era of surgical precision and tailored engagement. 

  While the insights from user behaviours empower businesses to optimise customer experiences, it’s essential to strike a delicate balance between personalisation and respecting user privacy. Ethical use of behavioural data ensures that the power of segmentation is wielded responsibly and in compliance, safeguarding user trust while enabling businesses to thrive in the digital age.

  What is behavioural segmentation ?

  Behavioural segmentation is a crucial concept in web analytics and marketing. It involves categorising individuals or groups of users based on their online behaviour, actions and interactions with a website. This segmentation method focuses on understanding how users engage with a website, their preferences and their responses to various stimuli. Behavioural segmentation classifies users into distinct segments based on their online activities, such as the pages they visit, the products they view, the actions they take and the time they spend on a site.

  Behavioural segmentation plays a pivotal role in web analytics for several reasons :

  1. Enhanced personalisation :

  Understanding user behaviour enables businesses to personalise online experiences. This aids with delivering tailored content and recommendations to boost conversion, customer loyalty and customer satisfaction.

  2. Improved user experience :

  Behavioural segmentation optimises user interfaces (UI) and navigation by identifying user paths and pain points, enhancing the level of engagement and retention.

  3. Targeted marketing :

  Behavioural segmentation enhances marketing efficiency by tailoring campaigns to user behaviour. This increases the likelihood of interest in specific products or services.

  4. Conversion rate optimisation :

  Analysing behavioural data reveals factors influencing user decisions, enabling website optimisation for a streamlined purchasing process and higher conversion rates.

  5. Data-driven decision-making :

  Behavioural segmentation empowers data-driven decisions. It identifies trends, behavioural patterns and emerging opportunities, facilitating adaptation to changing user preferences and market dynamics.

  6. Ethical considerations :

  Behavioural segmentation provides valuable insights but raises ethical concerns. User data collection and use must prioritise transparency, privacy and responsible handling to protect individuals’ rights.

  The significance of ethical behavioural segmentation will be explored more deeply in a later section, where we will delve into the ethical considerations and best practices for collecting, storing and utilising behavioural data in web analytics. It’s essential to strike a balance between harnessing the power of behavioural segmentation for business benefits and safeguarding user privacy and data rights in the digital age.

  

  Different types of behavioural segments with examples

  1. Visit-based segments : These segments hinge on users’ visit patterns. Analyse visit patterns, compare first-time visitors to returning ones, or compare users landing on specific pages to those landing on others.
     • Example : The real estate website Zillow can analyse how first-time visitors and returning users behave differently. By understanding these patterns, Zillow can customise its website for each group. For example, they can highlight featured listings and provide navigation tips for first-time visitors while offering personalised recommendations and saved search options for returning users. This could enhance user satisfaction and boost the chances of conversion.
  2. Interaction-based segments : Segments can be created based on user interactions like special events or goals completed on the site.
     • Example : Airbnb might use this to understand if users who successfully book accommodations exhibit different behaviours than those who don’t. This insight could guide refinements in the booking process for improved conversion rates.
  3. Campaign-based segments : Beyond tracking visit numbers, delve into usage differences of visitors from specific sources or ad campaigns for deeper insights.
     • Example : Nike might analyse user purchase behaviour from various traffic sources (referral websites, organic, direct, social media and ads). This informs marketing segmentation adjustments, focusing on high-performance channels. It also customises the website experience for different traffic sources, optimising content, promotions and navigation. This data-driven approach could boost user experiences and maximise marketing impact for improved brand engagement and sales conversions.
  4. Ecommerce segments : Separate users based on purchases, even examining the frequency of visits linked to specific products. Segment heavy users versus light users. This helps uncover diverse customer types and browsing behaviours.
     • Example : Amazon could create segments to differentiate between visitors who made purchases and those who didn’t. This segmentation could reveal distinct usage patterns and preferences, aiding Amazon in tailoring its recommendations and product offerings.
  5. Demographic segments : Build segments based on browser language or geographic location, for instance, to comprehend how user attributes influence site interactions.
     • Example : Netflix can create user segments based on demographic factors like geographic location to gain insight into how a visitor’s location can influence content preferences and viewing behaviour. This approach could allow for a more personalised experience.
  6. Technographic segments : Segment users by devices or browsers, revealing variations in site experience and potential platform-specific issues or user attitudes.
     • Example : Google could create segments based on users’ devices (e.g., mobile, desktop) to identify potential issues in rendering its search results. This information could be used to guide Google in providing consistent experiences regardless of device.

  

  The importance of ethical behavioural segmentation

  Respecting user privacy and data protection is crucial. Matomo offers features that align with ethical segmentation practices. These include :

  • Anonymization : Matomo allows for data anonymization, safeguarding individual identities while providing valuable insights.
  • GDPR compliance : Matomo is GDPR compliant, ensuring that user data is handled following European data protection regulations.
  • Data retention and deletion : Matomo enables businesses to set data retention policies and delete user data when it’s no longer needed, reducing the risk of data misuse.
  • Secured data handling : Matomo employs robust security measures to protect user data, reducing the risk of data breaches.

  Real-world examples of ethical behavioural segmentation :

  1. Content publishing : A leading news website could utilise data anonymization tools to ethically monitor user engagement. This approach allows them to optimise content delivery based on reader preferences while ensuring the anonymity and privacy of their target audience.
  2. Non-profit organisations : A charity organisation could embrace granular user control features. This could be used to empower its donors to manage their data preferences, building trust and loyalty among supporters by giving them control over their personal information.

  

  Examples of effective behavioural segmentation

  Companies are constantly using behavioural insights to engage their audiences effectively. In this section, we’ll delve into real-world examples showcasing how top companies use behavioural segmentation to enhance their marketing efforts.

  

  1. Coca-Cola’s behavioural insights for marketing strategy : Coca-Cola employs behavioural segmentation to evaluate its advertising campaigns. Through analysing user engagement across TV commercials, social media promotions and influencer partnerships, Coca-Cola’s marketing team can discover that video ads shared by influencers generate the highest ROI and web traffic.

     This insight guides the reallocation of resources, leading to increased sales and a more effective advertising strategy.

  2. eBay’s custom conversion approach : eBay excels in conversion optimisation through behavioural segmentation. When users abandon carts, eBay’s dynamic system sends personalised email reminders featuring abandoned items and related recommendations tailored to user interests and past purchase decisions.

     This strategy revives sales, elevates conversion rates and sparks engagement. eBay’s adeptness in leveraging behavioural insights transforms user experience, steering a customer journey toward conversion.

  3. Sephora’s data-driven conversion enhancement : Data analysts can use Sephora’s behavioural segmentation strategy to fuel revenue growth through meticulous data analysis. By identifying a dedicated subset of loyal customers who exhibit a consistent preference for premium skincare products, data analysts enable Sephora to customise loyalty programs.

     These personalised rewards programs provide exclusive discounts and early access to luxury skincare releases, resulting in heightened customer engagement and loyalty. The data-driven precision of this approach directly contributes to amplified revenue from this specific customer segment.

  Examples of the do’s and don’ts of behavioural segmentation 

  

  Behavioural segmentation is a powerful marketing and data analysis tool, but its success hinges on ethical and responsible practices. In this section, we will explore real-world examples of the do’s and don’ts of behavioural segmentation, highlighting companies that have excelled in their approach and those that have faced challenges due to lapses in ethical considerations.

  Do’s of behavioural segmentation :

  • Personalised messaging :
    • Example : Spotify
      • Spotify’s success lies in its ability to use behavioural data to curate personalised playlists and user recommendations, enhancing its music streaming experience.
  • Transparency :
    • Example : Basecamp
      • Basecamp’s transparency in sharing how user data is used fosters trust. They openly communicate data practices, ensuring users are informed and comfortable.
  • Anonymization
    • Example : Matomo’s anonymization features
      • Matomo employs anonymization features to protect user identities while providing valuable insights, setting a standard for responsible data handling.
  • Purpose limitation :
    • Example : Proton Mail
      • Proton Mail strictly limits the use of user data to email-related purposes, showcasing the importance of purpose-driven data practices.
  • Dynamic content delivery : 
    • Example : LinkedIn
      • LinkedIn uses behavioural segmentation to dynamically deliver job recommendations, showcasing the potential for relevant content delivery.
  • Data security :
    • Example : Apple
      • Apple’s stringent data security measures protect user information, setting a high bar for safeguarding sensitive data.
  • Adherence to regulatory compliance : 
    • Example : Matomo’s regulatory compliance features
      • Matomo’s regulatory compliance features ensure that businesses using the platform adhere to data protection regulations, further promoting responsible data usage.

  Don’ts of behavioural segmentation :

  • Ignoring changing regulations
    • Example : Equifax
      • Equifax faced major repercussions for neglecting evolving regulations, resulting in a data breach that exposed the sensitive information of millions.
  • Sensitive attributes
    • Example : Twitter
      • Twitter faced criticism for allowing advertisers to target users based on sensitive attributes, sparking concerns about user privacy and data ethics.
  • Data sharing without consent
    • Example : Meta & Cambridge Analytica
      • The Cambridge Analytica scandal involving Meta (formerly Facebook) revealed the consequences of sharing user data without clear consent, leading to a breach of trust.
  • Lack of control
    • Example : Uber
      • Uber faced backlash for its poor data security practices and a lack of control over user data, resulting in a data breach and compromised user information.
  • Don’t be creepy with invasive personalisation
    • Example : Offer Moment
      • Offer Moment’s overly invasive personalisation tactics crossed ethical boundaries, unsettling users and eroding trust.

  These examples are valuable lessons, emphasising the importance of ethical and responsible behavioural segmentation practices to maintain user trust and regulatory compliance in an increasingly data-driven world.

  Continue the conversation

  Diving into customer behaviours, preferences and interactions empowers businesses to forge meaningful connections with their target audience through targeted marketing segmentation strategies. This approach drives growth and fosters exceptional customer experiences, as evident from the various common examples spanning diverse industries.

  In the realm of ethical behavioural segmentation and regulatory compliance, Matomo is a trusted partner. Committed to safeguarding user privacy and data integrity, our advanced web analytics solution empowers your business to harness the power of behavioral segmentation, all while upholding the highest standards of compliance with stringent privacy regulations.

  To gain deeper insight into your visitors and execute impactful marketing campaigns, explore how Matomo can elevate your efforts. Try Matomo free for 21-days, no credit card required.