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Optics Express

Optics Express

  • Editor: J. H. Eberly
  • Vol. 5, Iss. 12 — Dec. 6, 1999
  • pp: 273–285
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Optical parallel database management system for page oriented holographic memories

Jian Fu, Marius P. Schamschula, and H. John Caulfield  »View Author Affiliations


Optics Express, Vol. 5, Issue 12, pp. 273-285 (1999)
http://dx.doi.org/10.1364/OE.5.000273


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Abstract

Current data recall rates from page oriented holographic memories far exceed the ability of electronics toeven read or transmit the data. For database management, we must not only read those data but also query them-computationally, a far more complex task. That task is very severe for electronics. We show here the rudiments of an optical system that can do most of the query operations in parallel in optics, leaving the burden for electronics significantly less. Even here, electronics is the ultimate speed limiter. Nevertheless, we can query data far faster in our optical/electronic system than any purely electronic system.

© Optical Society of America

1. Introduction

Most conventional massive database and knowledge base systems store data on magnetic or optical disks and employ indexing techniques to minimize disk accesses. Various clustering and accessing techniques are used to reduce response time. Even so, when the joint requirements of very large database (VLDB) and very short response times are imposed, existing technologies degrade considerably. Holographic memories can: (1) store hundreds of billions of bytes of data, (2) transfer them at a rate of a billion or more bits per second and (3) select a randomly chosen data element in 100 microseconds or less because of the 3-D recording and the parallel readout of an entire page of data at one time. There is no other memory technology that offers all three advantages [1

1. N. N. Vyukhina, I. S. Gibin, V. A. Dombrovsky, S. A. Dombrovsky, B. N. Pankov, E. F. Pen, A. N. Potapov, A. M. Sinyukov, P. E. Tverdokhleb, and V. V. Shelkovnikov, “A review of aspects relating to the improvement of holographic memory technology,” Opt. & Laser Tech. 28, 269–276 (1996). [CrossRef]

].

Fig. 1. The POHM concept is to vary a parameter P and record a hologram of a page with each P. Then when we restore the P value, we recall the corresponding page.

Each page can contain B bits (usually in the 105–106 range). Typically we store and recall P pages (usually in the 100 to 10,000 range). The random access time in P is T (usually in the 10-3 to 10-7 range). The total storage capacity is C=BP pages and the data random access rate is CÝ=C/T.

Let us illustrate with some achievable values such as B=105 bits, P=1000, and T=10-6 seconds. Then CÝ=1014 bits/s.

These are achievable, fairly conservative numbers. The critical number is CÝ. Ultra-fast electronics can achieve bit rates of 109 bits/sec, a factor of 105 away from keeping up. And that is just the readout capability. Query requires far more complicated operations with attendant massive slow down if done sequential.

If we hope to understand information almost as fast as we can recall it, we must abandon serial electronics in favor of parallel optics.

Driven primarily by the military need to find targets in noisy backgrounds, Fourier optical pattern recognition has improved immensely over the last 35 years. We now have compact systems with fast components implementing “optimum” masks on line [4

4. “Optimization Methods for Pattern Recognition,” in Optical PatternRecognition, Joseph L. Horner and Bahram Javidi, Eds., SPIE Optical Engr. Press, Bellingham, Washington, (1991),J. Shamir, Joseph Rosen, Uri Mahlaband, and H. J. Caulfield.

]. But, can we apply this parallel recognition method to PHM data pages?

The POHM DBMS problem will require all of those advances and more. This time, the targets are cooperative — as opposed to the military problem. We can design their signatures. Instead of roughly TV frame rates (~30/second), we seek page decisions much faster (~105/sec to 106/sec). SLMs (Spatial Light Modulators) with frame rates in this range are just now coming to market.

The purpose of this paper is to describe optical DBMSs for POHMs. With POHMs we can randomly access data many orders of magnitude faster than we can serialize and digest them electronically. Parallel optical DBMSs may offer the only hope of catching up with the data rate. In this paper we discuss the application of Fourier optical correlators in POHMs and focus on how construct a relational or object oriented DBMS for POHMs with fast access time, and high transfer rate.

2. Background

2.1 Prior work

Fig. 2. Selection and readout of pages

The deflector addresses a single hologram on the POHM and a page of data is written in parallel onto the output laser beam. An optically addressed SLM is placed at the output as an image amplifier to read the page into the optical system in parallel. A modulation pattern (intensity, phase, polarization, etc.) is produced by the SLM. The output beam comes from the portion of the SLM that is illuminated. If we illuminate the SLM addressed by the POHM with light from a second SLM (electrically addressed, intensity modulated), we can restrict entry into the optical system to those portions of the page of direct interest as shown in Fig. 3.

Fig. 3. Output Selection

Fig. 4. Pattern Selection

One of the approaches of selecting one of an array of holograms is to allow each hologram to store multiple images. For this method presented here, the JTC is a key factor that affects the system greatly. But we will show later in this paper that the JTC may not be as useful for ODBMSs as the sequential transform correlator (STC). We call the more-conventional approach sequential because the input and filtering planes encounter the laser beam sequentially. In the JTC, the input and filter (or reference) planes are encountered in parallel.

Fig. 5. Optoelectronic data filter

A major barrier to making such a system practical is the issue of how to produce the smart pixel arrays, such as 100x100 arrays. If the array size becomes large, electrically connecting the pixels with the remotely placed output devices becomes difficult and defeats the purpose of the optical interconnects. Such components can play a critical role in a POHM DBMS. We understand that prototype versions have been made of these systems have been built.

2.2 Fourier optical correlators

Optical pattern recognition, of course, is the core activity in database management for POHMs. We must (1) identify target (probably encrypted) patterns, (2) discriminate against other patterns, and (3) locate the pattern(s) so identified. None of those tasks is easy. Clearly we need an operation that is (1) limited in speed only by I/O (input/output), (2) capable of operating on whole pages in parallel (space-invariant filtering), and (3) readily reprogrammable to recognize new targets. The only systems we know which can do this are Fourier optical pattern recognizers.

Table 1. Correlator Architectures

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The mask (real or virtual) can vary from a simple matched filter to an on-line evolved optimum (by any arbitrary measure) filter.

We prefer the sequential transform system, because it has both speed and signal-to-noise advantages over the joint transform system in practice. Probably an electronically addressed SLM is the best choice as a mask. The matched filter is easy to calculate electronically by fast Fourier Transform (FFT) or can be optically determined by a dedicated system that performs a single Fourier transform. Electronic addressing allows either fast, local optimization by steepest descent or slow global optimization by genetic algorithms. Electronic addressing also permits rapid recall of masks from memory convenient. The details of our proposed sequential transform correlator are given in Sec. 3.

3. System overview

The optical DBMS for POHMs is shown in Fig. 6. Users require access the POHM for querying, updating, and generating reports. The DBM primarily exists for their use. The query is provided in electronic format. Of course, the POHM can not execute such a search. A dedicated computer works as a query controller. It has three tasks:

5. Recognize the query as a special case of something it knows how to do. That is, it must generate a virtual algorithm.

6. Convert the virtual algorithm into a physical algorithm, a series of physical acts to be performed.

7. Carry out the physical algorithm.

Fig. 6. Optical DBMS for POHMs

The query controller controls page accessing. The output light pattern must be operated upon by the DBMS hardware. As light does not operate on light, some electronics must be involved here. We can not do complex queries on multiple pages in parallel. Thus some sort of intermediate scratchpad memory is required. Finally, the answer to the query must be provided in a convenient electronic format. We imagine that this information will be read out from the scratchpad memory where it has been accumulated and processed. In our system, there are two forms of scratchpad memory: the Opto-Electronic RAM (random access memory, VIII in Figure 6), and the electronic cache (III) that also provides pointers to the opto-electronically stored data.

When we need a particular piece of information, we first check whether it is in the cache. If it is, we use the information directly from the cache; if it is not, the output of searching the POHM will be put in the cache. Then we can obtain what we want from the cache. A copy of the output will be put in the cache under the assumption that there is a high probability that it will be needed again. If the particular information is not in the cache, the computer will send three signals to POHM, flash addressed OERAM and optical pattern recognizer (OPR), respectively.

In a RAM, any addressable location in the memory can be accessed at random. That is, the process of reading from and writing into a location in a RAM is the same and consumes an equal amount of time no matter where the location is physically in the memory. With an optically flash addressed OERAM, we can enter the whole page in parallel and read only the pre-selected data into the cache memory.

After receiving the signal from the computer, the OPR will prepare to realize the pattern selection. A physical recognition mask will be used. If the mask can be an electronically addressed SLM, it can be evolved or at least optimized locally on line. Masks can be stored electronically and called into the SLM from memory quite rapidly. In other cases we will prefer an optically addressed SLM. We must avoid serializing the data on a page. An SLM is used to write the page onto a light beam. SLMs can be optically addressed, so serialization can be avoided. The output of OPR is then projected onto a 2-D charge-coupled device (CCD) or perhaps CMOS array that senses light-and-dark patterns and produces currents in response to the patterns, thereby reading all retrieved data. Now the data is in electronic form and send to cache through application specific integrated circuit (ASIC). With the CMOS system, perhaps these functions can be built into the detector array.

F(u,v)=[f(x,y)]=f(x,y)e2πi(xu+yv)dxdy
(1)

the Fourier transform of f(x,y). We now use the shift theorem

[f(xx0,yy0)]=e2πi(x0u+y0v)F(u,v)
(2)

That is, the shape and phase of the Fourier transform is independent of (x0,y0o,yo)-the displacement of the input. The displacement information is retained in the Fourier transform domain as a simple phase factor. Suppose now we multiply F(u,v) by a pattern M(u,v) that favors the Fourier transform of the pattern we seek and attenuates other patterns. Then the image plane will contain bright points everywhere the desired pattern occurs.

Fig. 7. Two physical algorithms for Fourier transforms

We can now compare the effectiveness of the two methods. The physical mask serial transform method and the joint transform method can both be optimized on line. The two methods both require two transform lenses. Both involve a detector array and Fourier transform lenses and SLMs. Both need two SLMs and detector arrays and system to locate the maximum. There are major problems with the joint transform method when there are numerous inputs, in that the dynamic range must be shared among all correlations. There are major problems with the physical mask also problems with the serial transform system if the mask is to be evolved quickly. There are, as of today, no good algorithms for this. On balance and at this moment, the dynamic range problem with the joint transform method appears more troublesome than the mask evolution problem with the serial physical mask transform method.

There are primitive physical algorithms for pages:

Algorithm 1 is called “page search.” We evolve a physical mask to recognize the desired pattern wherever it occurs.

Fig. 8. Intersection of data A and B

We can then form all sorts of logical merges such as A OR B, A AND B, A AND NOT B, etc. As A and B are found as 2D maps sequentially and B are found sequentially as 2D maps. We can do the merging in various ways. Although there are various ways to do this optically, it is probably easier to scan both maps synchronously and do the logical operations point-by -point electronically. It is easy to use one pass through both masks (stored on scratchpads) to mark patterns that satisfy the required logical pattern. That pattern can be written back into the scratchpad to allow the formation of more complex patterns such as (A OR B) AND NOT C or to store for subsequent output. To store outputs efficiently, we must recall all of the indicated information selected as an answer to the query. Of course, we want to compact this data - leaving no blank spaces corresponding to data not selected. Again, this is a task probably best done electronically.

• items likely to be concentrated on a small fraction of the pages,

• items expected to occur at an average rate of less than one per page,

• all other “fairly-unlikely” events, and

• all other events.

4. Critical Component analysis

4.1 Encryption

To make the most efficient use of electro-optical input transducers and the storage capacity of volume holographic memory, we need to apply an efficient encoding scheme. Pattern recognition has long strived to “read” human writing, be it in hand-written or machine generated form. This approach to encoding works well, most of the time, for humans, but electronic and optical processors still struggle to read it. Text has a number of problems:

1. A number of similar symbols, F is contained within E, O within Q, etc.,

2. Symbols that are similar to upside down or other symmetry reversed letters, A and V, and u and n are very difficult for optical correlators to distinguish, and

3. It takes a lot of information to form a letter (7x9=63 pixels minimum).

Any encoding scheme we choose has to be able to cleanly distinguish among all symbols in the character set, while reducing the data bandwidth required of the system.

We want to reduce the number of false identifications, even in the presence of noise. There are numerous schemes to encode symbols into orthogonal one- and two-dimensional spaces. We can use these as a staring point. We then stipulate that an auto-correlation should yield a higher peak than any cross-correlation. In effect, we require a Hamming distance proportional to the true-to-false ratio desired. There is a trade-off, however. The greater the Hamming distance, the more data have to be stored and transferred for a given amount of information.

Table 2. Examples of 1-D Hamming codes

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An excellent way of arranging these 1D codes in 2D is to use linear code strings separated by blank space, combined with 1D Fourier transform lenses.

4.2 Computer and data-bus considerations

The speed of the ODBM system is a function of several factors:

1. The electro-optic transducers,

2. The optical components,

3. The opto-electronic transducers, and

4. The front end host computer.

The electro-optic transducers, usually SLMs, are becoming increasingly fast with rates in the kHz regime. The speed of the optical passive components is never an issue. The opto-electronic transducers, CCD arrays, etc., can also be operated at high frame rates.

These high frame rates show the real limitation of a useful optical processor: the drive electronics. The data bandwidth of the optical system components is much greater than that of the electronic system components. If we assume a 512×512 array with a bit-depth of 1, each frame is 32 kB. At the normal video frame rate of 30, this is still a manageable 0.96 MB/sec, but at a frame rate of 10 kHz the required bandwidth becomes 320 MB/sec. One can use data compression to reduce the required bandwidth somewhat, but even for a 10 kHz frame rate the resulting bandwidth is well above what is available on present-day PCs. Some buses such as EISA, ISA, NuBus and VME cannot handle the bandwidth of video rate data, not to mention 10 kHz frame rates. The PCI bus (64 bit/66 MHz) is the best we can do with non-proprietary technology. It can handle a maximal theoretical throughput of 524 MB/sec [11]. The PCI but, however does not have symmetrical read and write speed With PCI bus systems one can hope to achieve frame rates between 6000 and 12000 per second for a bit depth of 1 compressed data. Proprietary standards such as found Silicon Graphics multiprocessor servers, can exceed 1 GB/sec. These proprietary standards, however, cannot be used as a basis for universal interface cards.

4.3 Optical hardware choices

The heart of the ODBM system is an optical pattern recognizer. There are two such generic systems: (a) the sequential transform correlator (STC) often also called the 4f correlator, even though a shorter 2f version is known and (b) the JTC discussed earlier.

The STC optically performs two Fourier transforms in sequence. The first lens Fourier transforms the input amplitude imposed by the mask (the data to be searched). In the Fourier domain a second mask (containing the complex conjugate of the Fourier transform of the pattern to be identified) multiplies the transformed data. The product is then Fourier transformed yet again to produce the correlation.

The advantages of a STC over a JTC are: (1) no intermediate processing, higher potential speed and (2) a higher signal-to-noise ratio. In a JTC, we must record the virtual masks for each object in the same medium. For multiple objects this places great strains on the available recording media.

The advantages of a JTC over a STC are: (1) More compact, and (2) no pre-stored filters. We prefer a system that combines the advantages of the JTC and STC. A single step system, similar to the JTC can be used to generate filters near real time. These filters can then be inserted into the STC system for pattern recognition.

Fig. 9. A diagram of the optical system. Note that the lens system uses cylindrical lenses for 1-D Fourier transforms. The component required for recording the holograms into the POHM are omitted for clarity.

At this point, this system is in the conceptual stage. However, from our analysis of the encryption and Fourier processing options, we have developed a preliminary design for the optical portion of the ODBM. A diagram of this system is shown in Fig. 9. Due to the choice of 1-D Hamming codes, we have chosen cylindrical lenses to perform the Fourier optical pattern recognition operations.

The POHM is recorded using the acousto-optical (AO) cell and light imaged from a SLM, not shown in Fig. 9, located above the POHM.

The POHM is read out by light passed through the AO cell. The light passes through a beamsplitter, and is either directly Fourier transformed onto the transform plane SLM, or passed through a register shifting SLM and detected by the OERAM. The OERAM is made up of an array of detectors and modulators with a small number of memory registers associated to each pixel. The OERAM can also be read out a page at a time and Fourier transformed onto the FT SLM. The FT SLM itself can be a 1D or 2D electronic SLM, or an AO cell.

If we choose to use an AO cell in the FT plane, we can change the filters at the same rate as the pages recalled from the POHM.

Once the product of the input and filter has been obtained, the results are again Fourier transformed and imaged onto a CCD array. In order to reduce the required bandwidth to the host computer, there we require thresholding circuitry packaged with the CCD. This optical system has not been built to date. However, we have performed numerical simulations of the response of various input fields and common FT filter types.

5. Conclusions

Since the data are already in optical form, the optical parallel DBMS for POHM processes it optically before conversion to sequential electronic form. This will have significant performance advantages, especially as data can be read from storage and queried at hundreds of megabytes per second.

Acknowledgments

This work is supported by SEA-DOE under contract no. DE-FG05-94Er25229.

References and links

1.

N. N. Vyukhina, I. S. Gibin, V. A. Dombrovsky, S. A. Dombrovsky, B. N. Pankov, E. F. Pen, A. N. Potapov, A. M. Sinyukov, P. E. Tverdokhleb, and V. V. Shelkovnikov, “A review of aspects relating to the improvement of holographic memory technology,” Opt. & Laser Tech. 28, 269–276 (1996). [CrossRef]

2.

C. Denz, T. Dellwig, J. Lembcke, and T. Tschudi, “Parallel optical image addition and subtraction in a dynamic photorefractive memory by phase-code multiplexing,” Opt. Lett. 21(4), 278–280 (1996). [CrossRef]

3.

T. Baer, “Relational Technology in the land of the giants,” Data Management/DBMS Software Magazine 61, Feb. (1996).

4.

“Optimization Methods for Pattern Recognition,” in Optical PatternRecognition, Joseph L. Horner and Bahram Javidi, Eds., SPIE Optical Engr. Press, Bellingham, Washington, (1991),J. Shamir, Joseph Rosen, Uri Mahlaband, and H. J. Caulfield.

5.

P. B. Berra, K.-H. Brenner, W. T. Cathey, H. J. Caulfield, S. H. Lee, and H. Szu, “Optical database/knowledgebase machines,” Appl. Opt. 29, 195–205 (1990). [CrossRef] [PubMed]

6.

F. R. Beyette Jr., K. M. Geib, C. M. St. Clair, S. A. Feld, and C. W. Wilmsen, “Optoelectronic Exclusive-Or Using Hybrid Integration of Phototransistors and Vertical Cavity Surface Emitting Lasers,” IEEE Photonics Tech. Lett. 5, 1322–1324 (1993). [CrossRef]

7.

P. A. Mitkas, L. J. Irakliotis, F. R. Beyette, S. A. Feld, and C. W. Wilmsen, “Optoelectronic data filter for selection and projection,” Appl. Opt. 33, 1345–1353 (1994). [CrossRef] [PubMed]

8.

A. B. VanderLugt, “Signal Detection by Complex Spatial Filtering,” Radar Lab., Rept. No. 4594–22-T, Institute of Science and Technology, The University of Michigan, Ann Arbor (1963).

9.

A. B. VanderLugt, “Signal Detection by Complex Spatial Filtering,” IEEE Trans. Inform. Theory IT-10, 139–145 (1964). [CrossRef]

10.

Louri and J. A. Hatch Jr. “An optical associative parallel processor for high-speed database processing,” Computer 27, 65–72, Nov. (1994).

11.

http://www.intel.com/network/performance_brief/pc_bus.htm

OCIS Codes
(100.4550) Image processing : Correlators
(200.3050) Optics in computing : Information processing
(200.4740) Optics in computing : Optical processing
(210.2860) Optical data storage : Holographic and volume memories
(210.4680) Optical data storage : Optical memories

ToC Category:
Research Papers

History
Original Manuscript: August 10, 1999
Published: December 6, 1999

Citation
Jian Fu, Marius Schamschula, and H. John Caulfield, "Optical parallel database management system for page oriented holographic memeories," Opt. Express 5, 273-285 (1999)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-5-12-273


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References

  1. N. N. Vyukhina, I. S. Gibin, V. A. Dombrovsky, S. A. Dombrovsky, B. N. Pankov, E. F. Pen, A. N. Potapov, A. M. Sinyukov, P. E. Tverdokhleb, and V. V. Shelkovnikov, "A review of aspects relating to the improvement of holographic memory technology," Opt. & Laser Tech. 28, 269-276 (1996). [CrossRef]
  2. C. Denz, T. Dellwig, J. Lembcke, and T. Tschudi, "Parallel optical image addition and subtraction in a dynamic photorefractive memory by phase-code multiplexing," Opt. Lett. 21(4), 278-280 (1996). [CrossRef]
  3. T. Baer, "Relational Technology in the land of the giants," Data Management/DBMS Software Magazine 61, Feb. (1996).
  4. "Optimization Methods for Pattern Recognition," in Optical PatternRecognition, Joseph L. Horner & Bahram Javidi, Eds., SPIE Optical Engr. Press, Bellingham, Washington, (1991), J. Shamir, Joseph Rosen, Uri Mahlaband H. J. Caulfield.
  5. P. B. Berra, K.-H. Brenner, W. T. Cathey, H. J. Caulfield, S. H. Lee, and H. Szu, "Optical database/knowledgebase machines," Appl. Opt. 29, 195-205 (1990). [CrossRef] [PubMed]
  6. F. R. Beyette, Jr., K. M. Geib, C. M. St. Clair, S. A. Feld, and C. W. Wilmsen, "Optoelectronic Exclusive-Or Using Hybrid Integration of Phototransistors and Vertical Cavity Surface Emitting Lasers," IEEE Photonics Tech. Lett. 5, 1322-1324 (1993). [CrossRef]
  7. P. A. Mitkas, L. J. Irakliotis, F. R. Beyette, S. A. Feld, and C. W. Wilmsen, "Optoelectronic data filter for selection and projection," Appl. Opt. 33, 1345-1353 (1994). [CrossRef] [PubMed]
  8. A. B. VanderLugt, "Signal Detection by Complex Spatial Filtering," Radar Lab., Rept. No. 4594-22-T, Institute of Science and Technology, The University of Michigan, Ann Arbor (1963).
  9. A. B. VanderLugt, "Signal Detection by Complex Spatial Filtering," IEEE Trans. Inform. Theory IT-10, 139- 145 (1964). [CrossRef]
  10. Louri, and J. A. Hatch Jr., "An optical associative parallel processor for high-speed database processing," Computer 27, 65-72, Nov. (1994).
  11. http://www.intel.com/network/performance_brief/pc_bus.htm

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