hifi 1, an ultra-low energy dsp for tws, bluetooth headset

WHITE PAPER

ContentsIntroduction .................................................................2

Battery Life, the Final Frontier ..............................2

A DSP for Next Generation Hearables, Wearables and Always-on Devices ......................3

Ultra-Low Energy Consumption ...........................4

System Configurations.............................................7

Comprehensive Software Support ....................10

The Many Applications that Benefit from HiFi 1 ............................................................................10

Conclusion .................................................................15

Appendix ....................................................................16

HiFi 1, an Ultra-Low Energy DSP for TWS, Bluetooth Headset, and Always-on Applications


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IntroductionThe popularity and consumer adoption of wireless hearables, such as TWS and Bluetooth headsets, is expected to grow rapidly, driven by a few key trends:

Demand for features and quality

Now accustomed to the convenience offered by wireless hearables, users are demanding higher sound clarity, and more features. The market is moving upwards with high-end hearables continuing to innovate, while their current generation features percolate down to mass-market devices.

Demand for reliability

Innumerable dropped calls and audible clicks in music bear testament to reliability issues in Bluetooth connections. This is a common annoyance and consumers expect reliability to closely match that of a wired headset.

AI in Audio

Algorithm innovation in AVS (audio, voice, and speech) has turned to AI and machine learning. Driven by new use cases and features that cannot easily be addressed by traditional DSP, device makers acutely feel the need to migrate such neural network algorithms from research to efficient on-device implementation. OEMs are also keen on future-proofing their designs to include AI capability for evolving use cases and applications—with the plan to employ late-stage software updates to offer new features that could be monetized even after building or deploying the product.

Voice control

“Voice as the new UI” is another mantra that we are living today. With the success of several models of household appliances that feature voice UI, consumers are gravitating towards products that offer this convenience. Voice control is now making its way into hearables where hands-free operations are highly desirable. For reasons of privacy, latency, and network availability, the preference now is to support local, on-device voice commands with no need to connect to the cloud. This preference is supported by technological advances that make local voice command recognition possible.

Together, these trends have created a perfect storm.

This white paper discusses these factors and presents a compact DSP that targets the needs of next generation hearables, wearables, and devices that need to be always listening or always on.

Battery Life, the Final FrontierAudio quality enhancement or added features inevitably lead to more battery drain. Form-factor constrained devices such as TWS earbuds may not be able to accommodate a larger battery to increase capacity. With battery life being one of the pet peeves of users, adding features becomes counterintuitive to user convenience. If constantly waiting for voice commands discharges the battery quicker, the device will be unusable often—convenience of voice erased by the inconvenience of frequent charging. It seems that OEMs are out of luck in wooing consumers with added features or improved audio quality.

Therefore, it behooves us all—algorithm developers, system designers, DSP designers, software developers—to do our part in increasing battery life, in the next step of technological evolution of wireless hearables.


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A DSP for Next Generation Hearables, Wearables, and Always-On DevicesThe current generation hearables are well served by HiFi 3, the most popular DSP for this market, having already shipped in billions of cores. However, the above considerations show that next generation hearables, wearables, and always-on or always-listening devices are better served by a DSP that targets their use cases. Considering this, Cadence introduced a compact DSP, HiFi 1, which specifically addresses the needs of such products.

HiFi 1 key benefits

HiFi 1 brings the following benefits for the target applications:

f Ultra-low energy consumption

f Keyword spotting efficiency and neural network support

f Compact footprint

f Efficient DSP and control code execution

The following sections discuss and quantify these; however, let us first examine the architecture.

HiFi 1 DSP Architecture

HiFi 1 Architecture is shown below:

HiFi 1 architecture is based on the efficient Harvard architecture, fetching instructions and data in parallel. HiFi 1 has two VLIW slots that execute instructions in parallel. Figure 1 illustrate the slot assignments for the various operations. Both slots can execute scalar and vector operations. Load/Store operations execute in slot 0 while integer/fixed point multiply accumulate (MACs) instructions execute in slot 1. To help with neural network workloads, loads also handle packed 8-bit data. Similarly, stores can truncate 16-bit data to 8-bits. The sign-extension for load and demotion for store operations are handled in the Load/Store units on the fly, without impact on execution cycle count. Neural network parameters that commonly use 8-bit weights can benefit from these facilities, gaining with storage density, increased performance, and lower energy.

Up to four 16x16 vector MACs, up to two 32x16 MACs, and one 32x32 MAC can be executed in one cycle. The SIMD register width for accumulations is 64-bit wide. Bitstream operations such as Huffman and Arithmetic coding used for accelerating codecs, also run in slot 1.

Figure 1: HiFi Architecture

HiFi Register File

Variable Encoding

Length

64 Bit

64 B

it

2 Execution Units (Slots)

Instruction Memory Interface Local (TCM) or Cache

General Register File

AR Register 16X32 bit

Data Memory Interface Local (TCM) or Cache

Register Mux

Load/Store + Execution

Load/Store Unit

Vector OPs

Scalar, Bit OPs

VFPU

Execution Unit

MACs

Vector OPs

Scalar OPs

VFPU

Instruction Decoder

AE_DR Register16x64 bit

VBOOL8x8 bit

AE_VALIGN4x64 bit


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The 16 data registers (AE_DR) used for DSP operations are 64-bit wide and support both integer/fixed point and single-pre-cision floating point formats. The 16 AR registers are part of the base XTENSA processor upon which HiFi 1 is built. These are mainly used for C program variables. Instructions and data are fetched in parallel over 64-bit busses, from either tightly coupled local memory (TCM) or caches or both, per system design. HiFi 1 ISA and architecture reduces memory access for both code and data. This helps to reduce memory energy and cache miss rate.

Vector floating-point unit

For applications that depend on floating point operations, SoC designer must select the optional single-precision vector floating point unit (SP-VFPU). The SP-VFPU is an improved pipeline-reduced design allowing denser operation packing and higher instruction parallelism to be achieved. Floating point operations can execute in either slot. Some selective float-ing-point operations can, by design, run concurrently in both slots.

Table 1 summarizes the high-level features of HiFi 1.

Ultra-Low Energy ConsumptionKeeping battery drain ultra-low is the prime focus of HiFi 1. Along with energy efficiency, cycle count efficiency for the different workloads also improves significantly. We discuss three classes of workloads below:

f Audio and Speech Codecs

f Signal Processing

f Neural Network

Audio and speech codecs

HiFi 1 adds specialized instructions (ISA) targeting Bluetooth Audio and Speech codecs, particularly LC3, which is the default codec for Bluetooth Low Energy (BLE)—see appendix for background on and reference for BLE and LC3, Low Complexity Communication Codec. Among the new HiFi 1 ISA is a set of instructions to accelerate LC3 arithmetic decoding. Slot assign-ments in HiFi 1 allow for higher degree of instruction level parallelism when running LC3 encode or decode.

Feature HiFi 1

Load Units 1

VLIW Slots 2

Scalar operations 2-slot

Accumulator Width 64-bit

Fixed Pt MACs per cycle

32x32 1

32x16 2

16x16/8x8 4

SP FPU (integrated, optional) Vector

Instructions for NN (low latency)

Arithmetic encoding/decoding Yes

AVS (Huffman and bitstream operation) Yes

Conditionals Yes

Coremark Energy Efficient Vector Boolean Register

Table 1: High-Level Features of HiFi 1


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The following chart shows that HiFi 1 is 18% more cycle efficient and 14% more energy efficient than HiFi 3 when decoding LC3.

Many of these improvements also help other Bluetooth codecs run more efficiently, outperforming HiFi 3 the current leader in this space across the different codecs.

Signal processing

Many pre- and post-processing steps in speech and audio are signal processing intensive. HiFi 1, although more compact and with lower instruction level parallelism than HiFi 3, closes the gap by improving many of the often used kernels.

For floating-point kernels, HiFi 1 offers an option to add a single-precision, 2-way vector floating point unit (SP-VFPU). The SP-VFPU in HiFi 1 improves performance and energy over the one with HiFi 3 because it is a lower latency design. Therefore, the tradeoff between HiFi 1 and HiFi 3 will depend on the mix of kernels used by the target set of algorithms.

As the chart shows, HiFi 1 improves both cycles and energy over HiFi 3 when running bi-quad filter, FFT, and matrix times vector operations. Whereas, HiFi 3 with its additional 32x32 MAC is able to perform better with the FIR filter. Even here, HiFi 3’s raw gain of 50%, expected due to its extra MAC unit, is limited to 24% by the architectural improvements in HiFi 1. Energy savings for this case is limited even further to only 13%.

0%

-5%

-10%

-18%

-14%

HiFi Energy gainHiFi 1 Cycle Count gain

HiFi 1 LC3 Decode Performance Compared With HiFi 3

-15%

-20%

-25%

bkfir_flt32_taps256_n80 bqriir_df1_flt32_bq5_n80 cfft_flt32_256 matXvec_f32_v1_m32xn128

HiFi 1 LC3 Floating-Point Performance Compared To HiFi 3

24%

13%

-5%

-18%

-7% -8%

-4%-5%

30%

25%

20%

15%

10%

5%

0%

-5%

HiFi 1 Cycle Count Gain HiFi 1 Energy Gain

-10%

-15%

-20%

-25%


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Neural networks

Neural Network workloads also show significant gains with HiFi 1, aided by the efficient handling of 8-bit data that is common in neural networks, as well as specialized NN ISA added for acceleration. Support for 8-bits also saves memory storage. Wake word detection with “Ok Google” TFLM benchmark (see appendix for Tensor Flow Light for Micro, TFLM reference, and background) improves energy and cycles by more than 60% and Person Detect benchmark improves by greater than 63% for both cycles and energy over HiFi 3. The Person Detect is a benchmark for vision based applications, i.e., for face detection. Here, HiFi 1’s ultra-low energy and high cycle efficiency shows its versatility. Whether it is keyword spotting or face detection, HiFi 1 can serve always-on applications very efficiently and with ultra-low energy consumption.

Compact footprint

The HiFi 1 core is 16% smaller than that of HiFi 3, making it a very compact core. This has the associated advantage that leakage, hence, static power consumption, is also lower, which in turn helps battery life. The energy and cycle count advantage of HiFi 1 over HiFi 3, despite being a more compact core, shows that HiFi 1 is indeed tailored for these applications, while HiFi 3 would still be better for general purpose DSP applications where compute requirements supersede energy savings.

HiFi 1 Cycle Count Gain

HiFi 1 Energy Gain

HiFi 1 Cycle Count Gain

HiFi 1 Energy Gain

HiFi 1 "OK Google" KWS Performance Compared with HiFi 3

-60% -62%

HiFi 1 "Person Detect" ML Inference Performance Compared to HiFi 3

0%

-15%

-30%

-45%

-60%

-75%

0%

-15%

-30%

-45%

-60%

-75%-64% -73%

0%

-5%

-10%

-16%

HiFi 1 StdCell Area Gain

HiFi 1 Stdcell Area Compared to HiFi 3

-15%

-20%

-25%


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System ConfigurationsThis section discusses three system configurations served by HiFi 1.

Single HiFi 1 core

Figure 1 shows a typical system configuration with a single HiFi 1 DSP core. Here, HiFi 1 is the always-on processor, and also the sole audio DSP in the system. This configuration is conducive to low cost, long battery life hearables with basic features. The ultra-low power DSP can be synthesized to run over a wide frequency range, allowing the system to scale frequency to meet workload demands using Dynamic Voltage-Frequency Scaling (DVFS). At low frequency settings, HiFi 1 can perform always-on listening and sensor fusion functions. In this state, power consumption and battery drain slow down to a trickle. A wake event signaled by user uttering the wake word or a change in the sensor states can allow system to respond by raising frequency to desired levels depending on workload, e.g., music playback or voice call. The system may go into power down mode after the call session or music playback is terminated, once again scaling DSP frequency down to the always-on, always-listening level. As the diagram shows, HiFi 1 can transition between an always-on state and performance state.

Performance energy tradeoff

While the same features—and more—can be achieved with a more powerful DSP such as HiFi 5, energy consumption by the DSP would be significantly higher. For example, LC3 would consume more energy if run on HiFi 5, as it doesn’t have the targeted enhancements for this application that HiFi 1 is designed with.

Advanced algorithms such as deep learning noise suppression, multi-modal sound analytics, and adaptive transparency require the more powerful DSP, HiFi 5 in this example. This would provide the highest listening and speech quality in the noisiest of environments. It would be desirable to provide the best of both worlds in the same product—energy savings for light workloads and high performance when the application demands it. The Big-little configuration below provides such a facility.

Big-little dual core configuration

Figure 2 shows the “Big-little” HiFi configuration, featuring both a HiFi 1 core with a HiFi 5 core—HiFi 1 in always-on, always-listening mode and the HiFi 5 for endowing the product with rich features. HiFi 1 provides music playback and speech encode/decode at ultra-low energy levels. HiFi 5 is used to run the differentiating features as discussed above. In this config-uration,

Call Mode

Keyword Detected Or Sensor State Change

Basic Command Mode

Session Ended by User or by Context Detection

Ph

one

Cal

lM

usi

c P

layb

ack

Freq High

Call Mode ANC,

Basic Post-Processing, Basic Noise Suppression,

Basic Sound Analytics, Basic Voice Command

Basic Voice Command

Processing Mode

Always-on State

Keywords Spotting, Sensor Fusion

Music Playback Mode ANC,

Basic Sound Analytics, Basic Voice Command

Voltage High

Freq LowVoltage Low

Ultra Low Energy Mode

Freq Very LowVoltage Very Low

Music Playback Mode

Freq MidVoltage Mid


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HiFi 1 performs:

f Always-on functions of the single-core configuration

f Play music or handle phone call

f ANC

HiFi 5 performs (as examples):

f Supports deeper voice command vocabulary or limited ASR

f Advanced noise suppression

f Rich post-processing of decoded audio (e.g., spatialization, personalization)

f Superior sound analytics

f Emotion analysis

Smart battery usage

The advanced features on HiFi 5 may be selectively and adaptively turned on/off based on context and environmental awareness, as detected by the HiFi 1 always-on sensor fusion. This would ensure that features are only enabled as needed, and not blindly turned on all the time, making for smart battery usage. HiFi 1 can be always on, keeping HiFi 5 in low power mode. HiFi 1 can “wake up” HiFi 5 to go into operational mode only when the user utters the key word or sensors request attention. Therefore, the Big-little HiFi combination provides the best of both worlds—longer battery life with HiFi 1, and rich features with HiFi 5.

HiFi 1 with AI Boost

When higher AI performance is needed, HiFi 1 can be paired with a neural network accelerator as diagram 3 shows. The Cadence NNE110 (see appendix) is designed to support a wide range of neural networks used in speech and audio applica-tions, though not limited to them. The SoC designer can tailor the NNE110 to match the application needs by choosing one of several MAC configurations. The stepped-up AI performance offered by NNE110 can enable significant additional functionality and high audio quality.

Along with sparsity support and weight compression, NNE110 provides very low energy inferencing, making it well suited to small battery powered devices such as TWS earbuds.

Choosing between Big-little and AI Boost configurations

Depending on whether the applications targeted are balanced in DSP and AI workloads, or more AI heavy, the System Architect can choose HiFi 1+NNE110 or HiFi 1 +HiFi 5 or HiFi 1+HiFi 5+NNE110. Note that, HiFi 5 in this example could be substi-tuted with HiFi 1, HiFi 3, HiFi 3z, or HiFi 4 to choose the desired performance on a sliding scale.

Figure 2: HiFi System Architecture: Single HiFi 1 core

BT RF+BBE HiFi 1

Ultra-low energy

Wake Up

Sensor-plex


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HiFi 1 as controller

The diagrams show a Bluetooth subsystem connected to the radio. This subsystem typically houses a dedicated controller core to manage the RF and Baseband pipeline. With HiFi 1 scoring high on Coremark benchmark, it is also efficient at running control code. Therefore, it can also subsume the Bluetooth controller function—this can help eliminate the dedicated controller core, reducing overall area as well as energy consumption. HiFi 1 software includes a choice of real-time operating systems and an opensource framework called XAF, to run control code and DSP code concurrently.

Controller HiFi 1 HiFi 1

Typical System: Modular ProcessingOptimal Processing

System

DSP + ControlFunctions

DSP FunctionsControl Code

Figure 3: HiFi System Architecture: “Big-little” configuration

BT RF+BBE + Controller HiFi 1

Ultra-low energy

Wake Up High Performance

domain

Sensor-plex

HiFi 5

Figure 4: HiFi System Architecture: HiFi 1 with AI Boost

BT RF+BBE HiFi 1

Energy-Efficient and Rich On-device AI

NNE110

Ultra-low energy

AI Boost at Low Energy Levels

Wake Up

Sensor-plex


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Comprehensive Software SupportHiFi 1 DSP maintains source code compatibility with the HiFi family of DSPs. Cadence offers the following libraries for HiFi 1:

f NDSP library: A large array of fixed-point and floating-point DSP kernels.

f NNLib Library: Neural Network libraries (NNlib)

f Codecs: A large array of speech and audio codecs for communication and music

Additionally, a large ecosystem of Cadence Partners provide HiFi optimized software. These are immediately available on the HiFi 1. When time to market and best-of-breed applications are essential, OEMs tend to find this ready-software base crucial to their product plans.

A note on floating-point

As most algorithms are developed in floating point, with MATLAB as the tool, the Vector FPU enables rapid “MATLAB to Optimized DSP” porting. This obviates the laborious and time-intensive steps of quantization, re-optimization, and validation, shortening time-to-deployment significantly. Some partners and customers have proven algorithms in floating-point form only. Therefore, it may be advantageous to include the optional VFPU in designs.

The Many Applications that Benefit from HiFi 1This section examines several target applications that can benefit from HiFi 1’s ultra-low energy profile.

Streaming devices

Consumers want the freedom to stream music to their Bluetooth hearables from multiple devices. Consumers can stream music from the usual connected devices—PCs, laptops, tablets and mobile devices. For on-the-go consumption, smart-watches can also stream music. HiFi 1 in these devices can provide the lowest energy transcoding to LC3, thereby, helping battery life on these streaming devices. Streaming of TV audio over Bluetooth for private listening, whether in front of the TV or in the backyard is a common use case and can benefit well from Bluetooth LE audio. This could be supported by HiFi 1 natively from the TV, or through an attached dongle—HiFi 1’s low power profile would easily fit within the USB power budget. A set-top box or soundbar can also choose to provide the same capability.

Sign Exponent Mantissa

Single Precision IEEE 754Floating-Point Standard

32 Bits

1 Bit 8 Bits 23 Bits


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Controller DSP

Both for streaming and hearable devices, HiFi 1, with many spare cycles, can act as the Bluetooth controller—power consumption and component count can be reduced in the quest for smaller, more efficient SoCs.

Context adaptive listening and communication

We experience different ambient noise and sounds in different circumstances, such as when riding a train, jogging outside, or working at a desk. Blocking out most or all noise and sounds is desirable for a great indoor listening experience but would pose a safety hazard when one is outside.

A one-size-fits-all-cases noise suppression and cancellation algorithm would not be suitable in all situations. Adapting sound processing based on user context and environment is key to better user experience. The HiFi 1 always-on sensor fusion aids in reliably detecting user activity. The user context, e.g., jogging outside or riding a train, can be classified and fed to the sound processing algorithm. Jaw motion can also be detected by sensors to distinguish between a user speaking and a neighbor close by. Armed with such information, the algorithm may allow lowered or increased ambient sound transparency, tune noise processing to different classes of extraneous sounds, and provide emergency alerts, such as glass breaking or an approaching ambulance.

Aided by the HiFi 1 ultra-low energy, always-on sensor fusion facility, context adaptation greatly improves user experience.

Hearing aids

Hearing aids, where comfort, size and battery life are critically important, represent yet another product category that could benefit from HiFi 1 and LC3. In fact, the development of LC3 was initially motivated by hearing aid companies. Whether medical or consumer grade, sound processing in Bluetooth-based hearing devices can compensate well for the listener’s hearing profile.

Figure 5: Adapting to a Variety of Contexts with AI

Figure 6: Hearing Aids embrace LC3 and AI


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Super-hearing

Even for the average person, Bluetooth earbuds that sport an external facing microphone can aid hearing in a noisy restaurant by separating conversation from the noise and surrounding babble. This makes for clearer conversations sans the straining. Earbuds can endow the wearer with super-hearing, i.e., the ability to zoom in on a conversation of interest while de-emphasizing all other sounds. HiFi 1’s frugal use of battery power ensures that users can enjoy super hearing for long periods of time.

Earbuds can enable Adaptive Noise Cancelation (ANC) even when one is not listening to music or in a call. This can shut out ambient noise, while the wearer enjoys super silence. HiFi 1 enables always-on ANC with its frugal use of battery.

With super hearing and super silence available to wearers at their voice command, wireless earbuds boost their utility and can become an “Always-Wear” accessory, further boosting demand.

Factory 4.0

The connected worker of Factory 4.0 will wear AR glasses, headsets, or other wearables to raise operational efficiency. With HiFi 1’s support of the LE Audio broadcast capability, management can reach any or all workers with one call. Keeping energy consumption low to contain battery weight is certainly critical to worker comfort. HiFi 1 in these wearables can support continuous 8-hour shifts with small batteries.

Figure 7: Factory 4.0 Worker with Always-on Processing in Headset


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Always-on sensor fusion for IoT, wearables, and mobile

HiFi 1 excels at sensor fusion, continuously monitoring, and performing signal processing with battery consumption at a trickle. This can allow the system to largely remain in power down mode while HiFi 1 efficiently looks for a wake-up event signaled by the sensors. Sensors such as IMUs, pressure sensors, IR sensors, ultrasound sensors, touch sensors, gas sensors, environmental sensors, and microphones, can all be continuously monitored for exceptional conditions or user intentions. IoT devices, wearables such as smart watches and bands and mobile devices are becoming the centers of intelligent sensing. With HiFi 1’s always-on processing, such events of interest would never be missed.

Presence detection for PCs and laptops

PCs and laptops reduce energy consumption or increase battery life by going into deep sleep or hibernation when idling for a long time. A user may walk away, and it would take several minutes before the machine invokes the battery saver mode, which wastes much energy. To bring the laptop or PC back to full operation mode takes time, and it starts with the user pushing a button to wake it up, which is an annoyance to the user. Lacking sensory intelligence, this is the best that the current crop of laptops and PCs can do.

Newer laptops and PCs offer always-on sensory perception of the user’s proximity and presence. The system detects a user receding and can invoke battery saving modes within seconds, spiraling down to hibernation if the user is absent for a while. Conversely, as the user is sensed approaching, service restoration activities can be started earlier, making for better user experience. With that, it is also less likely that a user will disable power save features to avoid the restart delay inconvenience.

Detecting a user’s presence is becoming imperative for data security reasons. Locking the system automatically when the user is sensed walking away, reduces the window of opportunity for someone around to snoop on or hack the system. This has also become a driving factor for inclusion of presence detection in newer laptops.

Sensors such as IR and ultrasound may be employed by laptops and PCs to detect user presence and proximity. Such detection needs to run continuously even in the lower power states and must itself consume very little power so as not to compromise the energy saving goals.

HiFi 1 is eminently suited to such always-on use case, providing adequate performance at ultra-low energy levels.

Always listening wake word monitoring

A system wakeup from sleep function, such as described above, may well be signaled by the user uttering the wake word. HiFi 1 has built in neural network facilitation functions to reliably detect wake words. Voice activity detection, noise filtering beam forming followed by wake word NN inferencing can help raise reliability in noisy environments, making it a go-to feature for consumers. Rather than touching, poking, and turning knobs, users may find it more convenient to say the wake word to activate the system. Simple voice commands following the wake word detection can be supported by scaling the frequency of operation of HiFi 1. More complex commands may be deferred to HiFi 5 if the Big-little configuration is employed.

HiFi 1 presents an ultra-low energy profile while in always-listening mode.

Always-on Presence Detection

Enhances user experience by reducing access timeReduces window of opportunity for snooping and hacking

Presence Detection

Figure 8: Always-On Processing Solution for Laptop Data Security


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Face detection

HiFi 1 is also capable of running face detection algorithms at reasonable frame rates and resolutions. Mobile devices, laptops, and security cameras can all benefit by offering this feature at ultra-low energy consumption.

With the Person Detect TFLM benchmark, HiFi 1 proves it can efficiently perform face detection. Whether it is detecting wake words or faces, HiFi 1 relieves devices of battery drain concerns.

Automotive personalization

Personalizing the unlocking and starting of automobiles is a desirable feature, which provides security and convenience. Such personalization is being enabled in newer cars using fingerprint recognition. To be always available, even with cars that are not operated for months at a time, the processing needs to be performed at low energy levels to not tax the car’s battery.

In this use case, no finger would be present for a vast majority of time, and therefore, the sub-system deploying HiFi 1 would spend all this time detecting the presence of a finger. HiFi 1 can perform such processing with extremely low energy consumption, helping to retain battery charge. When a finger is detected, HiFi 1 can raise its frequency and process finger-prints rapidly at low power levels, making it well suited for this application. Fingerprint recognition and speaker recognition can both be used together with HiFi 1 processing them concurrently to enable two-factor authentication, adding robustness to the security of the automobile.

Personalization can also help in automatically selecting the user’s preferences for seat adjustments, radio channel selections, speaker volumes, and infotainment display modes. Rapid speaker recognition with HiFi 1 can enable quick personalization of features that the automotive OEM wishes to offer.

Figure 9: Face Detection Figure 10: Always-Listening to Personalize in-car Settings

Hey Potato Ears!

No need to shout. I’m

always listenin’.


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Touchless UI for all

The post-novel coronavirus era will see user interfaces where physical contact is replaced with alternate methods. Gestures and voice are the two best alternatives, each of which can be used independently or together, as multi-modal interaction. HiFi 1 with its penchant for sensor fusion and keyword spotting can recognize basic gestures and voice commands, allowing the product to act and respond predictably. The Big-little HiFi configuration can be used to extend the voice command vocabulary for feature rich products—HiFi 5 would take over from HiFi 1 to handle the expanded vocabulary. This could be further extended to include limited NLP processing for more natural user interaction. It would also enable quick and direct access to functions, sparing the driver from navigating deep menus or fiddling with assortments of knobs and buttons.

Range hoods, microwaves, white goods, household appliances, industrial and test equipment are some product categories that could embrace touchless UI. In turn, they would obtain a boost in consumer and professional acceptance over devices that maintain only traditional interaction methods. Safety concerns stemming from virus fears may be the latest motivation for such technology, but the convenience of touchless interaction is timeless due to the inherent, hands-free convenience. HiFi 1 at its always-listening and always-on best barely consumes any energy while being responsive to the user, and HiFi 5 would unshackle users from the burden of touch and free them to talk naturally to the machine.

ConclusionHiFi 1 has pushed the boundaries of the ultra-low energy DSP segment, presenting a very compact footprint, yet retaining significant performance for traditional DSP workloads, and adding NN facilitations for wake word and Person Detect class of always-on workloads. HiFi 1 supports DVFS, which allows the system to go from low energy mode to high performance mode on the fly, allowing SoC designers and OEMs to differentiate their products with battery performance, user interactivity and quality auditory experience. HiFi 1 can be used as the sole DSP in a system or paired with a more powerful DSP such as HiFi 5. For deep machine learning applications, HiFi 1 can be paired with a Cadence Neural Network accelerator.

HiFi 1 is a DSP for a greener world, to serve a wide variety of energy-saving applications in markets ranging from consumer to automotive, industrial, and medical.

Figure 11: Always-On Voice Control for a Variety of Use Cases


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Appendix

Low Complexity Communication Codec, LC3

In September 2020, Bluetooth Sig adopted the Low Complexity Communication Codec, LC3, which approximately halves the data rate of existing Bluetooth codecs while maintaining comparable quality. Its lower energy requirements for streaming or playback and higher reliability due to lower bitrate make LC3 very attractive. Together with other features such as broadcast mode, audio sharing, and lower latency, these advantages are compelling industry-wide support spanning mobile, laptop, wearables, and hearables.

Link to Bluetooth Sig website: https://www.bluetooth.com/develop-with-bluetooth/join/

Tensorflow Light for Micro (TFLM) – see link: https://www.tensorflow.org/lite/microcontrollers

NNE110

https://www.bluetooth.com/develop-with-bluetooth/join/

https://www.tensorflow.org/lite/microcontrollers

https://www.cadence.com/en_US/home/tools/ip/tensilica-ip/tensilica-ai-platform/ai-boost.html

hifi 1, an ultra-low energy dsp for tws, bluetooth headset

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