Performance Optimization Stories - Before/After Analysis

RF Power Amplifier Efficiency Optimization

BEFORE

Class A Amplifier

27%

PAE

8.5W

Power Draw

85°C

Junction Temp

-35dBc

ACLR

// Fixed bias point
Vgs = -2.5V  // Class A operation
Vds = 28V    // Fixed drain voltage
Ids = 300mA  // High quiescent current

AFTER

Doherty with DPD

45%

PAE

5.2W

Power Draw

62°C

Junction Temp

-48dBc

ACLR

// Adaptive bias with envelope tracking
Vgs = f(Pin)  // Dynamic gate bias
Vds = ET(env) // Envelope tracking
Ids = 50mA    // Low quiescent current
DPD_coeff = LUT[temp, freq, power]

Optimization Techniques Applied

Doherty Architecture: Implemented asymmetric Doherty with 90° hybrid coupler for improved back-off efficiency
Digital Predistortion: 5th-order memory polynomial with adaptive LUT for linearization
Envelope Tracking: Dynamic supply modulation synchronized with RF envelope
Thermal Optimization: Improved heat sink design with vapor chamber cooling
Load Modulation: Active load-pull for optimal impedance across power levels

Week 1-2

Baseline Characterization: Measured Class A performance, identified efficiency bottlenecks at 6dB back-off

Week 3-4

Doherty Design: Simulated and implemented asymmetric Doherty, achieved 35% PAE at back-off

Week 5-6

DPD Implementation: Developed adaptive DPD algorithm, improved ACLR by 13dB

Week 7-8

Envelope Tracking: Integrated ET modulator, achieved additional 10% efficiency improvement

Efficiency vs Output Power

Overall Impact

65%

Efficiency Improvement

39%

Power Reduction

23°C

Temperature Reduction

$45K

Annual Energy Savings

Cache Hit Rate Optimization

BEFORE

Direct-Mapped Cache

52%

Hit Rate

180

Avg Cycles

2.8

CPI

// Simple modulo indexing
index = addr % CACHE_SIZE;
tag = addr / CACHE_SIZE;
// No replacement policy

AFTER

4-Way Set Associative + Prefetch

94%

Hit Rate

45

Avg Cycles

1.2

CPI

// Set-associative with LRU
set = (addr >> OFFSET) & SET_MASK;
way = find_lru_way(set);
// Stride prefetcher
prefetch(addr + detected_stride);

Optimization Techniques Applied

Set Associativity: 4-way set associative to reduce conflict misses
LRU Replacement: Pseudo-LRU implementation with 3 bits per set
Stride Prefetcher: Detects and prefetches regular access patterns
Victim Cache: 8-entry fully associative victim cache
Way Prediction: MRU-based way prediction for fast hits

Cache Performance Across Benchmarks

Overall Impact

42%

Hit Rate Improvement

4x

Performance Gain

57%

CPI Reduction

Cryptography Hardware Acceleration

BEFORE

Software Implementation

12ms

AES-256 (1MB)

450ms

Kyber KEM

85%

CPU Usage

// Software AES rounds
for(i = 0; i < 14; i++) {
  SubBytes(state);
  ShiftRows(state);
  MixColumns(state);
  AddRoundKey(state, key[i]);
}

AFTER

FPGA Accelerator

0.1ms

AES-256 (1MB)

3.7ms

Kyber KEM

2%

CPU Usage

// Hardware AES pipeline
always @(posedge clk) begin
  round[0] <= plaintext ^ key[0];
  for(i = 1; i <= 14; i++)
    round[i] <= aes_round(round[i-1], key[i]);
  ciphertext <= round[14];
end

Optimization Techniques Applied

Pipeline Architecture: 14-stage pipeline for AES rounds
Parallel Processing: 16 parallel AES engines for throughput
NTT Acceleration: Hardware Number Theoretic Transform for Kyber
DMA Engine: Zero-copy data transfer with scatter-gather
Constant-Time: Hardware ensures timing-attack resistance

Encryption Throughput Comparison

Overall Impact

120x

AES Speedup

121x

Kyber Speedup

10Gbps

Throughput

PCM Write Endurance Optimization

BEFORE

Direct Write

10⁶

Write Cycles

100%

Write Energy

500ns

Write Time

AFTER

Differential Write

10⁸

Write Cycles

15%

Write Energy

50ns

Write Time

Optimization Techniques Applied

Write Verification: Read-before-write to skip unchanged bits
Wear Leveling: Dynamic address remapping for uniform wear
Write Combining: Coalesce multiple writes to same line
Adaptive Programming: Adjust pulse parameters based on cell state
Error Correction: BCH codes to tolerate stuck cells

Overall Impact

100x

Lifetime Extension

85%

Energy Reduction

10x

Write Speed

ML Inference Optimization

BEFORE

FP32 CPU

45ms

ResNet-50

22 FPS

Throughput

65W

Power

AFTER

INT8 Accelerator

5.6ms

ResNet-50

178 FPS

Throughput

12W

Power

Optimization Techniques Applied

Quantization: INT8 with per-channel scaling
Systolic Array: 256×256 MAC array for convolutions
Weight Compression: Pruning + Huffman encoding
Layer Fusion: Conv-BN-ReLU combined operations
Memory Optimization: Tiling for on-chip SRAM reuse

Model Performance Comparison

Overall Impact

8x

Speed Improvement

5.4x

Power Efficiency

0.2%

Accuracy Loss

SoC Power Management Optimization

BEFORE

Always-On

2.5W

Idle Power

8.2W

Active Power

4hrs

Battery Life

AFTER

DVFS + Power Gating

0.7W

Idle Power

3.8W

Active Power

14hrs

Battery Life

Optimization Techniques Applied

Clock Gating: Fine-grained clock gating at module level
Power Domains: 4 independent power domains with isolation
DVFS: 8 operating points from 100MHz/0.8V to 1GHz/1.2V
Retention Registers: State retention during power gating
Workload Prediction: ML-based frequency scaling

Overall Impact

72%

Power Reduction

3.5x

Battery Life

15°C

Temperature Drop