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International Journal of Reconfigurable Computing
Volume 2010 (2010), Article ID 357978, 14 pages
Partial Reconfigurable FIR Filtering System Using Distributed Arithmetic
1Electrical and Computer Engineering Department, The University of New Mexico, Albuquerque, NM 87131, USA
2Microelectronics Research and Development Corporation, Albuquerque, NM 87110, USA
Received 2 March 2010; Revised 8 July 2010; Accepted 20 November 2010
Academic Editor: Viktor K. Prasanna
Copyright © 2010 Daniel Llamocca et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Dynamic partial reconfiguration (DPR) allows us to adapt hardware resources to meet time-varying requirements in power, resources, or performance. In this paper, we present two new DPR systems that allow for efficient implementations of 1D FIR filters on modern FPGA devices. To minimize the required partial reconfiguration region (PRR), both implementations are based on distributed arithmetic. For a smaller required PRR, the first system only allows changes to the filter coefficient values while keeping the rest of the architecture fixed. The second DPR system allows full FIR-filter reconfiguration while requiring a larger PR region. We investigate the proposed system performance in terms of the dynamic reconfiguration rates. At low reconfiguration rates, the DPR systems can maintain much higher throughputs. We also present an example that demonstrates that the system can maintain a throughput of 10 Mega-samples per second while fully reconfiguring about seventy times per second.
Dynamically reconfigurable systems offer unique advantages over nondynamic systems. Dynamic adaptation provides us with the ability to adapt hardware resources to match real-time varying requirements. The majority of the 1D FIR filtering literature is dominated by static implementations. Here, we use the term static to refer to both CMOS implementations (e.g., [1–5]) and reconfigurable hardware (nondynamic) (e.g., [6, 7]). Some implementations use the label reconfigurable in the sense of having the capability to load different filter coefficients on demand (e.g., [2–5]). In the context of this paper, such implementations are considered static since the underlying hardware is not changed or reconfigured.
For reconfigurable hardware, the most efficient implementations are based on Distributed Arithmetic (DA) . These filters have coefficients fixed or hardwired within the filter's logic. This approach allows fast and efficient implementations while sacrificing some flexibility, since coefficients cannot be changed at run time. Dynamic partial reconfiguration (DPR) can be used in this scenario to provide the flexibility of coefficients’ values changes without having to turn off the device and only rewriting a section of the configuration memory. The efficiency of DPR over the full reconfiguration alternative and the savings in terms of power and resources is a function of the relative size of the portion being reconfigured .
We consider a DPR approach that allows us to change the filter’s structural configuration and/or the number of taps. The proposed approach provides a level of flexibility that cannot be efficiently accomplished with traditional static implementations. In particular, we develop a dynamically reconfigurable DA-based FIR system that uses DPR to adapt the number and value of the coefficients, the filter's symmetry, and output truncation scheme. Two systems are presented that allow the flexibility to change all these filter's characteristics: (i) a system that only allows changes to the coefficients values and (ii) a system that allows changes to the number and value of the coefficients, the symmetry, and the output truncation scheme.
Previous research on dynamically reconfigurable FIR filters has focused on multiply-accumulate-based implementations and coarse reconfiguration. The first system described in this paper is based on dynamically reconfiguring at a coarse level, that is, the entire FIR filter. The second system is based on dynamically reconfiguring at the finest possible level, the LUTs that store the coefficients, with a small dynamic reconfiguration area. We have demonstrated a related, LUT-based approach in a dynamically reconfigurable pixel processor . The paper also explores different ways to execute dynamic partial reconfiguration and elaborates on the impact over reconfiguration time overhead of the different approaches.
This paper provides an extended version of the conference paper presented in . The paper has been extended to provide: (i) extended background information, (ii) more implementation details, (iii) extended methodology, (iv) architectural extensions to allow changes on the filter's internal structure, and (v) new results.
The rest of the paper is organized as follows: Section 2 presents background and related work. Section 3 describes the FIR filter core implementation. Section 4 introduces the dynamically reconfigurable system. Results and conclusions are presented in Sections 5 and 6, respectively.
2. Background and Related Work
Reconfigurable logic has established itself as a popular alternative to implement digital signal processing algorithms . Furthermore, a number of articles have been published on using DPR to implement different signal processing algorithms [9, 11, 13, 14]. In particular, [15–17] report different approaches for taking advantage of DPR in FIR filter implementations. The capability of reconfiguring a filter at run time is of special interest for applications such as wireless communications and software radio.
Hardware realizations of FIR filters can be divided into constant coefficients and multiplier-based implementations . In the latter case, DPR is mainly used to change a filter's overall structure [16, 17], or other filter-wide characteristic. At a higher level, DPR is also used to simply change the level of parallelism of an implementation by changing the number of filter cores in an application’s critical path. In all these cases, changes are usually initiated from a desire to implement a new filter, based on power or resources considerations, or simply to obtain new functionality. A change in coefficients does not require reconfiguration for this type of filter implementation. Thus, for these cases, DPR has milder constraints in terms of reconfiguration speed and reconfigurable logic partition.
The case of constant coefficients implementation is considerably more complex, since DPR is used to change inner characteristics of the filters (coefficients are not easily isolated within the filter structure). This requires more complex schemas to segment logic into reconfigurable tiles and more efficient reconfiguration mechanism in order to reduce the amount of time it takes to reconfigurable a filter.
DA filters in Xilinx FPGAs are introduced in [18, 19], where the authors exploit common characteristics between the Xilinx's FPGA architecture and the filter architecture. In , the authors present other approaches for flexible FPGA implementations of FIR filters by combining pipelined multipliers and parallel, distributed arithmetic.
In , the authors consider different DPR architectures for extending constant-coefficients approaches to implement adaptive filters. This relatively early study already provides insights on the advantages of using run-time partial reconfiguration to modify a filter's behavior at run time. The study used an earlier device (currently unavailable) and explored architectures different than DA, which were a natural fit for such device. Their results in terms of performance cannot be compared to our results due the inherent difference between the reconfigurable devices used.
In , the authors describe a self-reconfigurable adaptive FIR filter system composed of up to three multiplier-based filter modules. These modules can be reconfigured at run time by a control manager that uses System ACE to store and fetch the corresponding partial bitstream. This system only allows a full-filter reconfiguration instead of finer reconfiguration schemas such as coefficient-only reconfiguration. In this paper, speed results are not clearly presented. The authors report different reconfiguration overhead times for different filters that apparently occupy the same reconfigurable region in the device. These results are surprising, since reconfiguration time overhead depends mainly on the bitstream size, which depends on the size of the partial reconfigurable area, not on the number of resources used within that area. It is also worth mentioning that reconfiguration speeds reported are slower than speeds reported on other DPR papers [20, 21].
In , a similar system is described although in this case, it is not self-reconfigurable and uses an external PC to perform reconfiguration. Reconfiguration times reported are also considerably slower than other reported methods.
In , the authors describe a tool-flow to map applications to a self-reconfiguring application. The authors use a 32-tap MAC-based FIR filter as an example. The paper compares the performance of simply reloading coefficients by writing over specific registers and using DPR to reconfigure the whole filter. In this paper, the reconfiguration time overhead is large but dismissed as an acceptable handicap for the paper’s goals.
In general, the reconfiguration time overhead is an important factor in the evaluation of systems using DPR. Several approaches exist to deal with the overhead. One approach is to hide it by using efficient hardware scheduling strategies (e.g., ). A more simplified approach is to select carefully the elements of an architecture that requires reconfiguration for a desired change in functionality (e.g., [11, 21]). By doing so, one can reduce drastically the size of the partial bitstream used to execute the DPR, thus reducing the reconfiguration time overhead. Finally, there is also the approach of maximizing the access speed to the configuration memory (e.g., ). Unfortunately, this approach has a limit determined by the device. In the case of Virtex-4 FPGAs, the maximum speed is 3.2 Gbps (32 bit wide bus at 100 MHz). A combination of the last two approaches is used in this paper to deal with reconfiguration time overhead.
Our paper seeks to extend prior research in this area by primarily focusing on developing, analyzing, and improving DPR systems in terms of the dynamic reconfiguration rate on modern devices. This leads us to consider a DA implementation that allows efficient implementations with small hardware footprints on modern FPGA devices. Then, we consider a scalable approach where we have two systems: (i) a DPR system that allows for faster dynamic reconfigurations of coefficient values while fixing the number of taps and (ii) a second DPR system that allows flexibility in the number of taps, the filtering structure, and truncation characteristics while allowing for a slower dynamic reconfiguration rate.
3. Stand-Alone FIR Filter Core Implementation
A high-performance FIR implementation based on Distributed Arithmetic is described in this section (see also ). The approach was coded in VHDL, so as to achieve a level of portability. Specific LUT primitives are employed when the system is compiled in Xilinx devices. We will consider two dynamic realizations based on this core in Section 4.
The FIR filter module is shown in Figure 1. It shows the FIR filter module with its inputs, outputs, and parameters. Signal “E” controls the input validity. Clearing the register chain (“sclr” signal) at will is an important requirement when performing filtering on finite size signals.
Here, denotes the number of taps, NH represents the input/coefficients bitwidth, is the LUT input size (explained in next subsection). We also use OP for controlling the output truncation scheme: (i) LSB truncation then saturation, (ii) LSB and MSB truncation, and (iii) no truncation. We use the parameter format [NO NQ] to denote the fixed-point output format for NO bits with NQ fractional bits. The filter coefficients are specified in an input text file.
We define , for symmetric filters, and , for nonsymmetric filters. The inputs/coefficients format is set at , which restricts values to . As a result, the maximum number of output integer and fractional bits results
3.2. FIR DA Implementation
The Distributed Arithmetic technique rearranges the input sequence samples (be it or ) into vectors of length , which require an array of -input LUTs. This becomes prohibitively expensive when is large. For efficient implementation, we divide the filter into filter blocks , as illustrated in Figure 2. Each filter block works on coefficients requiring -input LUTs (each vector of goes to one -input LUT, see Figure 3). Table 1 summarizes the resources savings associated with the filter blocks approach. An advantage of using FIR filter blocks is that it allows for efficient routing while mapping the implementation to the specific LUT primitives found in an FPGA. As shown in , the approach is scalable in that can be easily ported to different LUT sizes.
To demonstrate the savings, we consider a particular example. Using the formulae of Table 1, for , , we have significant savings since . It does require an additional adder tree structure (see Figure 2). However, compared to the savings, the overhead is not significant.
A pipelined implementation of a symmetric filter block example is shown in Figure 3. Here, we have the parameters SYMMETRY = YES and NH = 8. It consists of an array of -input LUTs, an adder tree, shifters, and registers. The number of register levels is given by the following formula: The -input LUT subblocks are shown in Figure 4. Here, the output word size of each -input LUT is given by . It also shows its decomposition into LO L-to-1 LUTs, useful for efficient FPGA implementation. Xilinx FPGA devices contain L-to-1 LUT primitives with (Spartan-3, Virtex-II Pro, Virtex-4) and (Virtex-5). Thus, or are optimum values of choice. Moreover, as explained in  for Virtex-4, optimal LUT implementations can also be obtained for .
Figure 5 depicts the internal pipelined architecture of the adder tree that is used for adding the filter blocks outputs. The result is stored in an output register. The number of register levels of the adder structure is given by
Since we can quantize the LUT table values (i.e., the summations), rather than the coefficients, this FIR DA Implementation is slightly less sensitive to quantization noise than a normal implementation, with quantized coefficients.
The latency of the pipelined system is shown in Figure 6. The latency (input-output delay) is given by cycles, where REG_LEVELS is the number of register levels between the input and the output.
4. Dynamically Reconfigurable FIR Filtering System
We now extend the basic FIR filter core to be dynamically reconfigurable. We allow for the dynamic reconfiguration of both the number and the filter coefficients themselves in an embedded system. The basic system is shown in Figure 7. By means of dynamic partial reconfiguration, we turn a constant coefficient FIR filter into an adaptive FIR filter.
The basic approach requires that we prespecify the Partial Reconfiguration Region (PRR). We consider two dynamically reconfigurable realizations.(1)Coefficient-only reconfiguration: The PRR allows modifications to the filter coefficient values, while keeping the rest of the architecture intact.(2)Full-filter reconfiguration: The PRR allows modification to the number of coefficients, the coefficient values, and the filter symmetry.
We start by describing the system architecture and FIR filter dataflow, which are not affected by the PRR definition. Then, we explain each of the dynamic realizations by providing a detailing representation of the PRR in the context of the FIR filter architecture.
4.1. System Architecture
From Figure 7, we can see that the dynamic FIR core and the PowerPC (PPC) communicate using the high speed FSL bus. The Partial Reconfiguration Region (PRR) is dynamically reconfigured via the internal configuration access port (ICAP), driven by the ICAP controller core.
The DDRRAM stores volatile data needed at run time, for example, input streams, processed streams, and partial bitstreams. At power-up, SystemACE reads a Compact Flash (CF) Card that stores the partial bitstreams and input streams. The processed streams are written back to the DDRRAM. The Ethernet core provides reliable communication with a PC and allows us to get new partial bitstreams or new input streams and to send processed streams to the PC for its verification or storage. Also, it serves as an interface for throughput measurements and system status.
Figure 8 depicts the interfacing of the FIR filter processor and the PPC for both dynamic realizations. The FIR filter processor, as shown in Figure 7, consists of the FIR filter core and a control unit that provides interfacing with the 32-bitwide FSL bus. Figure 8 shows a special case when the filter input size is bits. Here, the input is processed sample by sample (one byte at a time). After 32 output samples are computed, they are transmitted through the FSL bus. Other input/output bit-width configurations require different logic and control.
We next provide a description of the different possible modes of operation. First, we note that an FIR filter with coefficients and NX input values can output a maximum of values. The three modes of operation are implemented through a finite state machine as follows.(i)Basic output mode: The system computes the first NX output values. This mode is useful for finite 1D signals.(ii)Symmetric output mode: The system computes the central NX output samples (i.e., in the range ). This mode is useful when performing 2D separable convolution on images.(iii)Streaming mode: with infinite number of input samples, that is, .
4.2. FIR Filter Processor Data Flow
The FIR Filter processor receives and sends 32 bits at a time via the FSL bus. Due to the FIFO-like nature of the FSL bus , the PPC processor sends a data stream to FIFOw to be grabbed by the FIR filter processor that in turn writes an output data stream on FIFOr to be retrieved by the PPC processor (see Figure 8).
We optimize FSL bus usage by letting the PPC write a large block of data on FIFOw. The FIR filter processor then processes the data and writes the results on FIFOr in a pipelined fashion. After reading all data in FIFOr, the PPC writes another large block of data on FIFOw, that is, the PowerPC is busy only when reading/writing each large block of data. In addition, the FIR filter processor starts reading the next available block of data on FIFOw right after writing a processed chunk of data on FIFOr. Each FIFO depth has been set to 64 words (32-bit words).
4.3. Dynamic Partial Reconfiguration Setup
Figure 8 presents two dynamically reconfigurable systems and the associated PRRs. In the full-filter reconfiguration case, we do not allow any changes to the I/O bit-width. Here, we note that a change to the I/O bit-width would also require a generalized FSL interface to be included in the PRR, further complicating the design. Despite the complexity of doing so, this will be of interest for allowing us to build a dynamic precision system.
The static region is defined by everything else outside the PRR, including FSL interface, FSL circuitry, peripheral controllers, and the FIR filter core static portion (coefficient-only reconfiguration).
All signals between the dynamic region (PRR) and the static part are connected by prerouted bus macros in order to lock the wiring. Also, the PRR I/Os are registered as the reconfiguration guidelines advise . To perform DPR, the partial bitstreams are read from a CF card and stored in DDRRAM. When needed, they are written to the ICAP port. This fairly simple technique is explained in .
For throughput measurement purposes, the partial bitstreams and the input set of streams reside on DDRRAM. The streams are sent to the FIR Filter processor, and the output streams are written back to the DDRRAM. This process is repeated with different partial reconfiguration bitstreams loaded at specific rates, so as to get different filter responses and measure performance as the reconfiguration rate varies.
4.3.1. Coefficient-Only Reconfiguration
In this dynamic realization, the dynamic region is made of -to-1 LUTs, resulting in a PRR with inputs and outputs. Figure 9 depicts the PRR along with the bus macros when SYMMETRY = NO, , , . The PRR is depicted in the context of the FIR filter core.
This realization is very useful for applications that only require filter coefficients modification, and it exhibits a smaller reconfiguration time overhead than the full reconfiguration case. Also, since only the LUT values are modified, the routing inside the PRR does not change. This has potential advantages in the area of run-time bitstream generation, as there is no need for run-time place-and-route operation. Fast routing is a very demanding task, and in most cases, it cannot be performed at run time .
4.3.2. Full-Filter Reconfiguration
In this case, the PRR involves the entire FIR filter core. It enables us dynamically modify the coefficients, number of coefficients, symmetry, and LUT input size. Figure 10 depicts the PRR along with the bus macros in the context of the FIR filter processor (with the FSL interface). We can see that the PRR has inputs and outputs.
5.1. Stand-Alone Fir Filter Core
Figure 11 shows hardware resource utilization as a function of the number of coefficients (), input bitwidth (NH), and symmetry (dotted lines: nonsymmetric filters, solid lines: symmetric ones). Also, we set , . Here, we use the XC4VFX20-11FF672 Virtex-4 device, with 8544 slices.
In addition, for each input bitwidth, we are considering the largest output format attainable (in the range ). The output format ([NO NQ]) plays a negligible role in resource consumption (a difference of at most 12 slices).
Regarding frequency of operation, the goal of 200 MHz minimum frequency of operation was attained in all cases.
In addition, an error analysis is performed for the same parameters. Figure 12 shows the relative error curves for three cases (input stream = 1024 sinusoid samples). The error metric is Figure 12 shows that in most cases the relative error is below 5%. The peaks correspond to FPGA values of zero and ideal values close to zero, resulting in a deceptive 100% error.
5.2. Embedded System
Results are shown using the following FIR Filter core parameters: , , , , , SYMMETRY = YES.
The system is implemented on the ML405 Xilinx Development Board that houses a XC4VFX20-11FF672 Virtex-4 FPGA. The PPC is clocked at 300 MHZ and the peripherals run at 100 MHz.
In order to improve performance, the DDRRAM memory space is cached. Also, the dynamic systems are tested in the basic output mode; that is, only the first NX outputs are considered.
5.2.1. Hardware Resource Utilization
Results for this section depend on the specific dynamic realization. Tables 2 and 3 show hardware resource utilization for two DPR systems: (i) coefficient-only reconfiguration and (ii) full-filter reconfiguration. It shows the static region, dynamic region and the entire system resource usage. The module “PRR interface” is the gluing static logic needed to join the static and dynamic regions.
As expected, the overall resource utilization is about the same. What varies is the static region size, which is larger in the coefficient-only reconfiguration case.
Table 4 shows the reconfiguration size and its partial bitstream size. Note that the PRR in the first case is somewhat larger than expected (about 62% of the second case). This can also be appreciated in Figure 13 that shows the dynamic region (PRR) for both realizations, which are functionally the same.
The reason for the large PRR in the first case is the large number of required bus macros I/Os. In the coefficient-only reconfiguration case, the system needs access to the LUTs (see Section 4.3.1). As a result, for the special case shown, we require inputs and outputs.
As explained in Section 4.3.2, in the second case (full-filter reconfiguration), we only need inputs and outputs. So, the PRR in the first case is larger than what it is actually needed for the L-to-1 LUT array, thereby wasting hardware resources in order to accommodate the large number of bus macros I/Os.
5.2.2. FIR Filter Processor Performance Bounds
The maximum throughput of this particular FIR filter processor () is given by Note that since the system is pipelined, there is an initial setup delay that becomes negligible over time. Actual throughput depends on many factors, such as cache size, PPC instruction execution, and FSL usage. Note that the maximum throughput of (5) cannot be attained since the PPC cannot read and write into the FIFOs at the same time.
5.2.3. Reconfiguration Time
Table 5 shows the reconfiguration time for 3 scenarios. Both dynamic realizations are included. In our setup, called Scenario 1, we used the Xilinx ICAP core and obtained a reconfiguration average speed of 3.28 MB/s. The reconfiguration time of Scenario 2 is computed based on the speed results reported in . The dramatic improvement in reconfiguration lies on the use of a custom ICAP controller, DMA access, and burst transfers. Scenario 3 is the maximum theoretical throughput, which for the Virtex-4 is 400 MB/s .
5.2.4. Dynamic Performance
We use software timers to measure the elapsed time from the moment we start reading the input stream from DDRRAM until the processed stream is written back on DDRRAM. We are considering sinusoids as our inputs. Here, we refer to Section 4.3 for some of the details that will be discussed in this section.
In order to evaluate the dynamic performance of the system, we use a stream of 102400 samples (1 sample = 8 bits). The stream is processed a number of times (100 runs). Within the 100 runs, partial bitstreams are loaded at a specific rate. Each partial bitstream amounts to a different filter response.
Note that for the coefficient-only reconfiguration case, we only load a different set of coefficient values.
For the full-reconfiguration case, we switch between a filter with coefficients and one with coefficients. The PRR size is defined to be sufficiently large so as to allow implementation of the larger filter; that is, the filter case. The filter with requires only one fewer latency cycle (3). As a result, the static performance improvement of the smaller filter is not significant.
We report the average throughput over the 100 runs. Here, we define the dynamic reconfiguration rate in terms of the inverse of the number of samples that are being processed prior to a hardware reconfiguration. For better visualization, we report throughput in terms of the number of processed Mega samples per second (MSPS). This corresponds to the inverse of the reconfiguration rate.
Figures 14 and 15 show the dynamic performance over 100 runs for both dynamic realizations. There are 3 curves that correspond to the 3 scenarios shown in Table 5. In the limit, at zero reconfiguration rate, we have static performance. The performance results converge for the static case.
From Figure 14 (coefficient-only reconfiguration), we see that for Scenario 1 (our actual measurements), the static performance resulted in 29.25 MSPS. At the maximum reconfiguration rate (1 reconfiguration every stream), the dynamic performance resulted in 6.1651 MSPS. The other curves (Scenarios 2 and 3) provide performance bounds based on the static performance and reconfiguration speeds of Table 5.
We can appreciate that the dynamic performance of the full-filter reconfiguration case is slightly lower than the coefficient-only reconfiguration. This is due to differences in the PRR size. But as we increase the number of samples before a reconfiguration, or use a scenario other than the first one, this effect is less noticeable.
As expected, the dynamic performance heavily depends on reconfiguration speed and input stream size. Better reconfiguration speeds offset the reconfiguration time overhead (Scenarios 2 and 3). We have the same effect for smaller dynamic regions. The slower reconfiguration rates due to longer data streams help to offset the reconfiguration overhead as well.
In Table 6, we present the full-filter reconfiguration system throughput as a function of the time between reconfigurations. It is quite clear from the results that even for the slowest scenario, we can maintain throughputs over ten MSPS while dynamically reconfiguring seventy times per second.
5.3. Experimental Results with ECG Processing
We present an example application for electrocardiogram (ECG) characterization (R-wave detection). Here, we consider coefficient-only reconfiguration for implementing a 3-channel, 1D filterbank. We make use of the embedded system detailed in Section 5.2. Each channel filter is symmetric, with 32 8-bit coefficients for 8-bit I/O, using truncation (saturation) arithmetic for the outputs. Our approach here is to implement a variation of the ECG processing algorithms presented in .
ECG signal processing is of great interest for emergency applications, including the detection of cardiac arrhythmias  and stenosis assessment for atherosclerotic plaque video analysis . A popular approach based on  is to use the outputs of a Wavelet filterbank for ECG analysis.
As in Wavelet analysis, we design a dyadic filterbank to cover the entire, discrete frequency space. We have a high-pass filter with a positive frequency pass-band from to , a band-pass filter from to , and a low-pass filter for frequencies up to . For each channel filter, we consider efficient implementations using 32 8-bit coefficients. The magnitude response of the designed filterbank is shown in Figure 16.
For testing the implementation, we use the first recording (record 100) from the MIT arrhythmia database . In this record, we have 2 channels with 650 K samples sampled at 360 Hz and quantized at 11-bits over a 10 mV range. We further quantized the input down to 8 bits, downloaded them to the DDRRAM using the Ethernet core and tested using the procedure outlined in Figure 17.
Based on , we implemented a simple R-wave detection algorithm. For detection, we look for thresholds in the outputs. In the example of Figure 18, we threshold as follows: low-pass filter (), band-pass (), and high-pass (). This results in perfect R-wave detection for the first 5 cycles of the second channel (1500 samples). We refer to  for more details on how to adjust thresholds in such algorithms for near-perfect results verified over the entire database. Our goal is to simply demonstrate the DPR FIR system on real signals.
The detection algorithm is included in the embedded PowerPC software routines, and the resulting signal is stored into the DDRRAM. We note that performance improves with larger input signals (see Figure 14). The detection algorithm is performed at the end of the operations and takes about 80 ms. Dynamic reconfiguration of a channel filter requires 13.1 ms. At a sampling rate of 360 Hz, the system allows significant time for implementing real-time detection algorithms and DPR. As a result, the number of samples that are processed prior to reconfiguration can be significantly reduced. By processing every 2000 samples, the processing rate stands at 4.62 MSPS (2000 samples takes 140 ms to process). Thus, after 5.5 seconds spent in acquiring 2000 samples, we get a detection response in 140 ms.
5.4. Comparison with Other PR Systems For FIR Filtering
The majority of previously reported work on FIR filtering is based on multiply-and-add approaches [14, 16, 17, 22]. In , the authors reported a reconfiguration time of 1.5 ms for changing the coefficients and their number on a Virtex-II (74.7 KB bitstream). In [16, 17], the authors presented a DPR FIR system that only allowed for changes in the number of coefficients. Reconfiguration time for 8794 slices for a 20-tap filter required 700 ms. The filter presented in  most closely resembles our FIR filter: 32-taps, 8-bit coefficients, 8-bit input, but with multiply-and-add approach. It required 1985 LUTs for a 13.1 ms reconfiguration time. We can change the entire filter using a 83 KB bitstream for a reconfiguration time of 25.3 ms.
As mentioned earlier, for FPGA implementations, the distributed arithmetic presented here is far better suited than these multiply-and-add approaches. DA approaches allow for efficient use of hardware resources. Beyond this, multiply-and-add approaches tend to have fixed input/output characteristics as opposed to the flexible, dynamically reconfigurable arithmetic representations presented here.
We presented two efficient dynamic partial reconfiguration systems that allow us to implement a wide range of 1D FIR filters. Requiring a significant smaller partial reconfiguration region, the first system allows changes to the FIR filter coefficients while keeping the rest of the architecture intact. Using a larger partial reconfiguration region, the second system allows full-filter reconfiguration. This system can be used to switch between FIR filters based on power, performance, and resources considerations.
For both systems, the required partial reconfiguration region is kept small by using Distributed Arithmetic implementations. System performance is evaluated in terms of the dynamic reconfiguration rate. For a representative example, it is shown that we can process over ten Mega samples per second while dynamically reconfiguring about seventy times per second. The introduction of faster dynamic reconfiguration controllers can lead to much higher throughputs for the same number of reconfigurations per second. Alternatively, we can maintain much higher throughputs at much lower reconfiguration rates.
The results have encouraged us to explore the use of dynamically reconfigurable filtering for digital image and video processing applications. As seen from the results of this paper, it is possible to dynamically reconfigure at real-time frame rates. For such applications, the DPR systems can be extended to separate implementations of 2D dynamically reconfigurable filterbanks.
The research presented in this paper has been funded by the Air Force Research Laboratory under Grant no. QA9453-060C-0211.
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