diff --git a/doc/erko_teaser_talks.txt b/doc/erko_teaser_talks.txt
index c6c183887bae5894160d58f5c750671c6616c955..d878d06e3f98a84e2b8e2d11d2857cd9cf65d5d4 100644
--- a/doc/erko_teaser_talks.txt
+++ b/doc/erko_teaser_talks.txt
@@ -27,26 +27,153 @@ at university.
1) Teaser talk: Quantization in LOFAR2.0 Station Firmware
* floating point - fixed point - integer
- . 2**+127 -------------------------- 1. ------------------ 2**-127
- <n bit int>
- . <n bit fxp> fraction only
- <n bit fxp> with fraction
- <n bit fxp> . scaled
- . two complement, so range e.g. -8 to +7 for 4 bit value
+ <p bit exponent>
+ . max -----<w bit mantissa>----- 1. ------------------ min floating point
+
+ <w bit int> integer
+
+ . <w bit fxp> fixed point fraction only
+ <w bit fxp> fixed point with fraction
+ <w bit fxp> . fixed point scaled
+
+
. format:
- - unsigned : u(w, p)
- - signed : s(w, p)
-
-* Operations (*, +) cause bit growth
- - rounding (to remove LSbits)
- . truncation: int(x), // (-7 // 6 = -2, 7 // 6 = 1)
- . half away: python2, matlab
- . half up
- . half to even: python3, SDPFW
- - clipping or wrapping (to remove MSbits)
- . intermediate beamlet sum in BF uses wrapping
- . final subband output and beamlet output use clipping
+ - w = number of bits including sign bit
+ - p = number of bits after point (p > 0) or before point (p < 0)
+ - p = 0 for integer
+ - u = unsigned : u(w, p)
+ - s = signed : s(w, p)
+
+
+ * two complement representation of signed numbers
+
+ - E.g. range -8 to +7 for 4 bit value:
+
+ -1 1111
+ -2 1110
+ -3 1101
+ -4 1100
+ -5 1011
+ -6 1010
+ -7 1001
+ -8 1000
+ +7 0111
+ +6 0110
+ +5 0101
+ +4 0100
+ +3 0011
+ +2 0010
+ +1 0001
+ 0 0000
+
+ - E.g. range -4 to +3 for 3 bit value:
+
+ -1 111
+ -2 110
+ -3 101
+ -4 100
+ +3 011
+ +2 010
+ +1 001
+ 0 000
+
+ - MSbit is the sign bit
+ - Sign extension of MSbit
+ -1 +1
+3 bit 111 001
+4 bit 1111 0001
+8 bit 11111111 00000001
+
+ . overflow wrapping, e.g. with 4 bit:
+ 7 + 1 = -8
+ -8 - 1 = +7
+ -(-8) = -8, negating most negative
+
+ . product has 2w bits, if most negative * most negative is excluded then 2w - 1 bits:
+
+ -8 * -8 = 64 = 01000000 = largest positive requires 2w bits
+ 7 * +7 = 49 = 00110001 = largest positive fits in 2w-1 bits
+ -8 * +7 = -56 = 11001000 = smallest negative fits in 2w-1 bits
+
+* Operations (*, /, +, -) in the digital processing cause bit growth
+ - Keep as many LSbits as needed to preserve sensitivity
+ - Keep as many MSbits as needed to preserve dynamic range
+
+ Note:
+ . typically we can avoid using a / b in VHDL:
+ . do a * 1/b if b is a constant
+ . use bit shift if b is power of 2
+ . similar for a % b to get remainder after division by b
+ side note: beware of a % b == 1, result is language dependent when a or b is negative
+
+
+* Removing LSbits (unused resolution, insignificant bits)
+ - truncation by discarding LSbit is free in logic:
+
+ . E.g. 4 bit value discard 2 LSbit
+
+ -1 1111 -1 11
+ -2 1110 -1 11
+ -3 1101 -1 11
+ -4 1100 -1 11
+ -5 1011 -2 10
+ -6 1010 -2 10
+ -7 1001 -2 10
+ -8 1000 -2 10
+ +7 0111 +1 01
+ +6 0110 +1 01
+ +5 0101 +1 01
+ +4 0100 +1 01
+ +3 0011 0 00
+ +2 0010 0 00
+ +1 0001 0 00
+ 0 0000 0 00
+
+ Corresponds to:
+ . shift right by n bits >> : -5 >> 2 = -2, 5 >> 2 = 1
+ . divide // 2**n : -5 // 4 = -2, 5 // 4 = 1
+ . floor: floor(-5 / 4) = -2, floor(5 / 4) = 1
+
+ ==> discarding b LSbits in logic = a >> b = a // 2**b in python
+ ==> a // b = floor(a / b) for integer a, b
+
+ - rounding LSbits costs logic:
+ + rounding at whole
+ . ceil()
+ . int(x): int(-5 / 4) = -1, int(5 / 4) = 1
+ ==> beware: int(x) != floor(x)
+
+ + rounding at half
+ . rounding scheme for half, becomes more significant when fraction has fewer bits
+ . round() : language dependent
+ . half up
+ . half away: python2, matlab --> no DC bias
+ . half to even: python3, SDP Firmware --> no DC bias and no power bias
+ -1.500000 --> -2
+ -0.500000 --> 0
+ 0.500000 --> 0
+ 1.500000 --> 2
+ 2.500000 --> 2
+
+* Removing MSbits (unused dynamic range)
+ - wrapping
+ . truncation by discarding MSbits is free in logic
+ ==> beware wrap(x, 4 bits) != x % 2**4
+
+ . wrap operator is distributive similar as the modulo operator:
+ (a + b) % n = [(a % n) + (b % n)] % n
+ ==> intermediate beamlet sum in SDP digital beamformer uses wrapping
+
+ - clipping
+ . clipping to min and max costs logic
+ ==> subband output for uses clipping
+ ==> final beamlet output in SDP output to CEP use clipping
+
+* Reuse standard requantization component
+ - supports removing MSbits and LSbits
+ - to ensure same Q scheme is used consistently throughout the SDP firmware
+
* SDP signal path
- Task: Preserve sensitivity of the ADC input and maintain sufficient dynamic range
@@ -56,6 +183,7 @@ at university.
. beamlets (BF processing gain)
- BST
- beamlet output (8 bit samples to CEP)
+ - Show where Q is applied, always same component is used for Q
- Figure of internal signal levels
. dBFS
. SNR, P_quant
@@ -63,6 +191,52 @@ at university.
. coherent input (sine), incoherent input (sky noise, weak astronomical signal burried in
noise)
+ Received signal at ADC consists of (q = one quantization step):
+
+ . RFI, typically narrow band
+ . sky noise >> q
+ . receiver noise < sky noise
+ . quantization noise = 0.29 q
+ . astronomical signal << q, buried in the sky noise
+
+ The sky noise sets the nominal input level for the ADC --> sigma_sky = 1 bit is enough for 2% SNR degredation
+
+ The RFI sets the maximum input level for the ADC --> -50 dBFS margin to avoid overflow (clipping)
+
+ Used FPGA build in multipliers of 18b * 18b or 27b * 18b
+
+ FIR filter
+ - Consists of multiply (real coefficients) accumulates (taps)
+ - DC gain of the filter is 1 so sum does not grow beyond 30b
+ - Treat the ADC data as integers and the coefficients as fixed point numbers between +-1
+ - W_adc = 14b and W_coeff = 16b yields product of 30b
+ - Round 30b filter output to 14b
+ - Next step (FFT) can fit 18b input, so round to 18b to keep some more accuracy
+
+ FFT
+ - Consists of multiply (complex twiddle factors) and accumulates (butterfly)
+ - Narrow band input (sine wave) has gain of 0.5 = -1b
+ - FFT has processing gain of sqrt(N_point) = 5b for N_point = 1024
+ ==> Need 14b -1 + 5 = 18b to fit the subband samples
+ ==> internally the FFT calculates with 26 bits data to avoid accumulation of rounding errors
+
+ Subband calibration using complex subband weigths
+ - Consists of complex multiply to calibrate fine gain and fine delay differences between antenna inputs
+ - Initially we used 18b subbands, but that yielded artifacts in the SST
+ - Now we keep 26b subbands until after applying the subband weigths ands the round to 18b
+
+ Beamforming using complex weights and summation of N antenna inputs
+ - std level of coherent input increases with N_ant, e.g. RFI in the beam, the (weak) astronomical signal in the beam
+ - std level of incoherent noise increases with sqrt(N_ant), therefore we can round log2(sqrt(N_ant)) bits
+
+ - Subband statistics
+ . relate SST level to subband level
+ - Subband weights
+ . calibrate for gain and phase (= fine delay) differences between signal inputs
+ . can also calibrate cross-polarization between X and Y per antenna (used in Disturb)
+ . no equalization accross the subband frequencies is done
+ - Beamlet weights
+
* Implementation details
- Use separate function to do DFT for two real ADC inputs with complex FFT
- Spectral inversion to have incrementing subband indexes and frequencies in all Nyquist zones
@@ -72,6 +246,8 @@ at university.
- Interally extra LSbit inside PFB and before applying the weights, see try_round_weight.py
* Conclusion:
+ - int(), floor(), ceil(), round(), //, % differ in details and can depend on sign, try
+ to be sure per language
- Fixed point arithmetic uses less FPGA resources (multipliers, RAM, logic) than floating
point, but requires carefull bookkeeping or the fixed point position in the FW
implementation.