Back in the nineties the digital signal processor (DSP) had its heyday. It was a completely different beast compared with CISCs (complex instruction set computers) like Intel’s Pentium or RISCs (reduced instruction set computer) like ARM’s IP cores. DSPs allowed complex mathematical algorithms to be processed in real-time based on the fundamental mutliplier-accumulator structure of their internal architecture. Mostly these signal processors had a 16-bit fixed-point word length with a very basic feature set. Their distinctive clout was speed combined with low power - a boon for all types of embedded applications requiring real-time response. Yet their restricted word length made programming them an art, something for maths whizz kids who could work with integers just as well as with floating point numbers. And then at some point, stand-alone DSPs simply disappeared off the processor map. What happened?
With each reduction in size of semiconductor processing nodes, clock speeds moved into the gigahertz range whilst supply voltages and power consumption dropped dramatically. Lower power levels and longer word lengths enabled newer processor types and categories to extend their reach into the domain of rigorous real-time requirements, formerly a unique terrain for digital signal processors. Multiple cores on the same dice were suddenly feasible and the stand-alone DSP simply got gobbled up in the process. What was once a stand-alone math starlet enjoying the limelight became reduced to a common (and essential) block on a larger system-on-chip (SoC).
Recently I’ve noticed the term “DSP” appearing more frequently again in the semiconductor world. The trail leads back to sensors; in particular, sensors as they are used in smartphones. First -generation devices featured three or four sensors to ensure fluid interaction with their touch screens: a proximity sensor to turn off the display during a call for saving power and preventing contact with the ear or face; an ambient light sensor for the best reading experience under all types of lighting conditions; an accelerometer to sense the orientation of the phone and switch between portrait and landscape modes accordingly. With each new device generation, further sensors joined the fold. Recent smartphones are often blessed with over ten such environment watchdogs.
Sensors provide information on what the user is currently doing. Combined with location intelligence, smartphones can react intelligently to the user’s activity and current surroundings. Continuous sampling, storage and processing of sensor data is necessary in order to keep track of what is happening to the device, its user and whereabouts. Keeping the phone’s main processor- one of the battery hogs - on all the time, makes no sense. Enter the power efficient coprocessor, often termed sensor hub, or motion processor and sometimes even DSP.
By adding an additional processor with scanty energy demands operating separately from the main, power-hungry applications processor, the continuous flow of sensor data can be analyzed all the time, even when the phone itself is asleep. Sharp readers will contest why use a processor geared towards blistering speed (read DSP) if sensor data, like readings of the temperature or magnetic field, arrive at a snail’s pace? Yet most calculations are all about sensor fusion, or using multiple sensors inputs to determine something really useful. Take indoor navigation as an example. The usual satellite GPS signal may not be available yet seamless navigation might still be required. Using combined data from the accelerometer, gyroscope, magnetometer and pressure sensor, the DSP implements a mathematically complex Kalman filter to accurately estimate the user’s position from previous bearings (dead reckoning algorithm) and simultaneously compensates for a number of tricky sensor anomalies such as offset, gain, non-linearity and noise. Such an intelligent sensor hub provides rich soil for further smartphone differentiation to take root as algorithms and smartphone apps combine motion, physiological (e.g. heart rate, voice analysis, …) and environmental data in completely new ways. This new trend brings the digital signal processor (DSP) back into the spotlight, reestablishing it prowess from its former glory days as a highly power-efficient mathematical engine.
Layed off by semiconductor advances
With each reduction in size of semiconductor processing nodes, clock speeds moved into the gigahertz range whilst supply voltages and power consumption dropped dramatically. Lower power levels and longer word lengths enabled newer processor types and categories to extend their reach into the domain of rigorous real-time requirements, formerly a unique terrain for digital signal processors. Multiple cores on the same dice were suddenly feasible and the stand-alone DSP simply got gobbled up in the process. What was once a stand-alone math starlet enjoying the limelight became reduced to a common (and essential) block on a larger system-on-chip (SoC).
Smartphones and their sensors
Recently I’ve noticed the term “DSP” appearing more frequently again in the semiconductor world. The trail leads back to sensors; in particular, sensors as they are used in smartphones. First -generation devices featured three or four sensors to ensure fluid interaction with their touch screens: a proximity sensor to turn off the display during a call for saving power and preventing contact with the ear or face; an ambient light sensor for the best reading experience under all types of lighting conditions; an accelerometer to sense the orientation of the phone and switch between portrait and landscape modes accordingly. With each new device generation, further sensors joined the fold. Recent smartphones are often blessed with over ten such environment watchdogs.
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Each new smartphone generation features more sensors |
Sensors provide information on what the user is currently doing. Combined with location intelligence, smartphones can react intelligently to the user’s activity and current surroundings. Continuous sampling, storage and processing of sensor data is necessary in order to keep track of what is happening to the device, its user and whereabouts. Keeping the phone’s main processor- one of the battery hogs - on all the time, makes no sense. Enter the power efficient coprocessor, often termed sensor hub, or motion processor and sometimes even DSP.
Fusing sensor data to predict location
By adding an additional processor with scanty energy demands operating separately from the main, power-hungry applications processor, the continuous flow of sensor data can be analyzed all the time, even when the phone itself is asleep. Sharp readers will contest why use a processor geared towards blistering speed (read DSP) if sensor data, like readings of the temperature or magnetic field, arrive at a snail’s pace? Yet most calculations are all about sensor fusion, or using multiple sensors inputs to determine something really useful. Take indoor navigation as an example. The usual satellite GPS signal may not be available yet seamless navigation might still be required. Using combined data from the accelerometer, gyroscope, magnetometer and pressure sensor, the DSP implements a mathematically complex Kalman filter to accurately estimate the user’s position from previous bearings (dead reckoning algorithm) and simultaneously compensates for a number of tricky sensor anomalies such as offset, gain, non-linearity and noise. Such an intelligent sensor hub provides rich soil for further smartphone differentiation to take root as algorithms and smartphone apps combine motion, physiological (e.g. heart rate, voice analysis, …) and environmental data in completely new ways. This new trend brings the digital signal processor (DSP) back into the spotlight, reestablishing it prowess from its former glory days as a highly power-efficient mathematical engine.
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