IEEE Solid-State Circuits Magazine - Summer 2015 - 72

h(t )

Undistorted
Sampling Points
Distorted
Waveforms

-3T

-2T

-T

Figure 2: Zero intersymbol interference condition.

distortion, as shown in Figure 2, yet
the relevant information present at
sampling instants with interval k/T is
preserved.
The more significant and enormous
bandwidth-saving opportunities for
sensor applications facilitated by feature sampling can be clearly illustrated
with an example from the speech
processing domain: speech detection
and speech analysis are often done
using features representing the averaged energy content of exponentiallyspaced bandpass frequency bands
(called mel-scaled frequency bands)
as a coarse form of spectrum analysis. As the speech signal, typically
corrupted by background noise, is neither sparse nor has finite degrees of
freedom, standard sub-Nyquist sampling techniques are not appropriate.
Yet significant sampling rate reduction can be achieved by introducing

a feature-sampling ADC. For example,
voice activity detection can be implemented by extracting features in the
analog domain representing the energy
profile of mel-scaled frequency bands,
averaged across 20 ms frames [16].
Good speech detection performance
has been demonstrated using eight
such mel-frequency features, coarsely
computed in the analog domain. The
resulting feature rate corresponds to
8/20 ms = 400 Hz, or more than an
order of magnitude below the Nyquist
rate of the audio signal. Hence, the
speech signal information bands are
enhanced through analog analytics,
while distorting background noise
is discarded (if out of band) or suppressed (if in band).
Clearly, feature-extracting ADCs
enable drastic sampling rate reduction, beyond what is possible in traditional lossless analog-to-information

converters. It is important to note
that feature-extracting ADCs exploiting analog analytics are not suggested as a replacement for classical
converters. In many applications
where reconstruction of a signal is
required, such as multimedia applications, a standard Nyquist approach
as shown in Figure S2(a) is still
required. Feature-extracting ADCs
are most appropriate for applications
which do not involve reconstruction
of original signals, or can function
as a smart wake-up front end to such
systems. Yet these new opportunities
also come with many remaining challenges, at both the systems and circuit levels, which will be discussed in
the following sections.

System Challenges
and Opportunities for
Feature-Extracting ADCs
A typical system architecture for
feature-extracting data converters is
represented in Figure 3. One or more
feature channels project the analog
input onto a feature-enhancing basis.
Subsequently, a subset of all feature
channels is scanned and sampled into
the digital domain for further processing and potential signal classification. This new paradigm comes with
a set of new system challenges. First,
the choice and design of the analog
feature-enhancing filters cannot be

Feature Control/Activation
Feature-Enhancing Filter
Context
Detection

Feature-Enhancing Filter
...
Feature-Enhancing Filter
Feature-Enhancing Filter

Figure 3: Feature-extracting ADC architecture.

su m m e r 2 0 15

IEEE SOLID-STATE CIRCUITS MAGAZINE

Feature Scan-Mux

Feature-Enhancing Filter
SubNyquist
ADC

Relevant
Feature
Bandwidth

Feature
Processing/
Classifier

Table of Contents for the Digital Edition of IEEE Solid-State Circuits Magazine - Summer 2015