Echo Alexa Customer Support Number USA Alexa offers your customers a new way to interface with technology – a convenient UI that enables them to plan their day, stream media, and access news and information. If you’re planning on building a device with the Alexa Voice Service (AVS), you’ll want to ensure you have the right amount of central processing unit (CPU) power, memory, and flash storage to ensure your product brings a delightful hands-free Alexa experience to your customers. In this blog post, we provide examples of existing AVS device solutions that can be used as a guide for sizing up CPU, memory, and storage for a headless voice-forward device with microphone(s) and speaker(s). Please note this blog does not cover CPU or memory requirements for screen-based devices, tap-to-talk Alexa implementations, smart home use cases or Alexa Calling and Messaging. Sizing Up CPU Sizing an embedded system processor is a combination of science and art. A common but outdated convention is to use Dhrystone MIPS (Million Instructions Per Second), or DMIPS, as a measure of processor performance relative to the 1970-era DEC VAX 11/780 minicomputer. DMIPS are generally reported as DMIPS/MHz for the typical MIPS of a processor at a given MHz. The Dhrystone benchmark suffers from several shortcomings, as performance metrics can vary considerably for the same hardware using different compilers, compiler optimization settings that optimize away large portions of the test code, and wait-state delays for reading from memory. Other benchmarks suffer similarly. At the end of the day, your real-world application is the final judge of actual performance. For more insight reach us at Echo alexa customer support number. CPU and Memory Work Together An Alexa client application requires host processor cycles for tasks such as wake word detection, data compression and decompression. It also requires memory to buffer outbound and inbound audio streams for Text-To-Speech (TTS) and music playback. Compiler options for code size optimization and techniques for code compaction to generate smaller executables that more easily fit in limited amounts of memory on embedded systems are commonly used to keep costs to a minimum. These constraints impose a need for considering both CPU and memory when developing and optimizing embedded systems software. Programming styles on large versus small computer systems can also vary and affect required processing power and memory. The adage that compute cycles are cheaper than human programming cycles as it generally applies to large computer systems does not necessarily
translate well to small embedded systems. While it’s true that System on Chip (SoC) or System On Module (SOM) capabilities continue to increase and lower in cost, competitive markets and tight margins impose a need for close scrutiny of the overall cost of the system, especially when millions are being produced. Techniques such as code profiling help isolate portions of programs that utilize more CPU or memory. Focusing optimization in these areas is a first step in reducing the overhead of software components and ultimately enable a lower Sizing It All Up The amount of CPU, memory, and storage can vary substantially for different processor architectures and operating systems. Optimization techniques play a huge role in reducing required system resource capacity. Table 1 below shows examples of processor, memory, and flash storage headroom values in Alexa applications for Alexa conversation (voice responses, Flash Briefings, weather) and streaming media use cases. The vendor column indicates whether the device originates with a processor vendor (V), AVS Systems Integrator (SI), or an AVS Development Kit (Dev Kit). For more help reach us Echo Alexa customer support number USA.