🧠 Level RISC-V processor — AI/ML improvement plans¶
This document describes artificial intelligence and machine learning based improvements that can be added to the Level RISC-V processor.
📋 Table of contents¶
- Neural Branch Predictor (GShare + Perceptron)
- Neural Cache Prefetcher
- Learned Cache Replacement Policy
- Load/Store Stride Predictor
- Hazard Prediction Unit
- Workload-Aware Power Management
1. Neural Branch Predictor¶
1.1 Current state¶
- File:
rtl/core/stage01_fetch/gshare_bp.sv - Current algorithm: Tournament predictor (GShare + bimodal + loop predictor)
- Components:
- GHR (Global History Register)
- PHT (Pattern History Table) - 2-bit saturating counter
- BTB (Branch Target Buffer)
- RAS (Return Address Stack)
- Loop Predictor
- IBTC (Indirect Branch Target Cache)
1.2 Proposed improvement: perceptron branch predictor¶
┌─────────────────────────────────────────────────────────────────┐
│ Perceptron Branch Predictor │
├─────────────────────────────────────────────────────────────────┤
│ │
│ Global History (GHR) Perceptron Weights Table │
│ ┌─┬─┬─┬─┬─┬─┬─┬─┐ ┌────────────────────────┐ │
│ │1│0│1│1│0│1│0│1│ ───► │ W0 W1 W2 ... Wn Bias│ │
│ └─┴─┴─┴─┴─┴─┴─┴─┘ └────────────────────────┘ │
│ │ │ │
│ │ ┌────────────────────┘ │
│ │ ▼ │
│ │ ┌─────────────────────┐ │
│ └───►│ Dot Product + Bias │ │
│ │ Σ(xi * wi) + w0 │ │
│ └─────────────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────────┐ │
│ │ Sign(result) > 0 │───► Taken │
│ │ Sign(result) ≤ 0 │───► Not Taken │
│ └─────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────┘
1.3 Design details¶
// Perceptron branch predictor module
module perceptron_bp #(
parameter HISTORY_LEN = 32, // Global history length
parameter TABLE_SIZE = 256, // Perceptron table size
parameter WEIGHT_BITS = 8 // Weight bit width
)(
input logic clk_i,
input logic rst_ni,
input logic [XLEN-1:0] pc_i,
input logic [HISTORY_LEN-1:0] ghr_i,
input logic update_i,
input logic actual_taken_i,
output logic predict_taken_o,
output logic high_confidence_o
);
// Perceptron weight table
logic signed [WEIGHT_BITS-1:0] weights [TABLE_SIZE][HISTORY_LEN+1];
// Prediction threshold (theta = 1.93 * history_len + 14)
localparam int THETA = (193 * HISTORY_LEN) / 100 + 14;
// Index calculation
logic [$clog2(TABLE_SIZE)-1:0] idx;
assign idx = pc_i[$clog2(TABLE_SIZE)+1:2];
// Dot product calculation
logic signed [WEIGHT_BITS+$clog2(HISTORY_LEN)+1:0] sum;
always_comb begin
sum = weights[idx][0]; // Bias
for (int i = 0; i < HISTORY_LEN; i++) begin
if (ghr_i[i])
sum = sum + weights[idx][i+1];
else
sum = sum - weights[idx][i+1];
end
predict_taken_o = (sum >= 0);
high_confidence_o = (sum > THETA) || (sum < -THETA);
end
// Training logic
always_ff @(posedge clk_i) begin
if (!rst_ni) begin
// Initialize weights to 0
for (int i = 0; i < TABLE_SIZE; i++)
for (int j = 0; j <= HISTORY_LEN; j++)
weights[i][j] <= '0;
end else if (update_i) begin
// Update only on misprediction or low confidence
if ((predict_taken_o != actual_taken_i) || !high_confidence_o) begin
// Update bias
if (actual_taken_i)
weights[idx][0] <= sat_inc(weights[idx][0]);
else
weights[idx][0] <= sat_dec(weights[idx][0]);
// Update history weights
for (int i = 0; i < HISTORY_LEN; i++) begin
if (ghr_i[i] == actual_taken_i)
weights[idx][i+1] <= sat_inc(weights[idx][i+1]);
else
weights[idx][i+1] <= sat_dec(weights[idx][i+1]);
end
end
end
end
endmodule
1.4 Integration plan¶
- Add the perceptron module inside
gshare_bp.sv - Integrate it as a third option in the tournament predictor
- Use a meta-predictor to choose among GShare, bimodal, and perceptron
1.5 Expected benefit¶
- Misprediction rate: 5–15% reduction
- Area cost: ~2KB SRAM (256 entries × 33 weights × 8-bit)
- Latency: 1 cycle (with parallel dot product)
2. Neural Cache Prefetcher¶
2.1 Current state¶
- File:
rtl/core/mmu/cache.sv - Current prefetch: None
- Cache organization: N-way set associative, PLRU replacement
2.2 Proposed improvement: perceptron-based prefetcher¶
┌─────────────────────────────────────────────────────────────────┐
│ Neural Cache Prefetcher │
├─────────────────────────────────────────────────────────────────┤
│ │
│ ┌────────────────┐ │
│ │ Access History │ │
│ │ ┌────────────┐ │ ┌─────────────────────────────────────┐ │
│ │ │ PC₀, Δ₀ │ │ │ Perceptron Network │ │
│ │ │ PC₁, Δ₁ │ │───►│ ┌─────┐ ┌─────┐ ┌─────┐ │ │
│ │ │ PC₂, Δ₂ │ │ │ │ W₀ │ │ W₁ │ │ W₂ │ ... │ │
│ │ │ ... │ │ │ └──┬──┘ └──┬──┘ └──┬──┘ │ │
│ │ │ PCₙ, Δₙ │ │ │ │ │ │ │ │
│ │ └────────────┘ │ │ └────────┼────────┘ │ │
│ └────────────────┘ │ ▼ │ │
│ │ ┌────────────────┐ │ │
│ │ │ Σ + Threshold │ │ │
│ │ └───────┬────────┘ │ │
│ └─────────────┼───────────────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────────────┐ │
│ │ Prefetch Decision │ │
│ │ • Delta to prefetch │ │
│ │ • Confidence level │ │
│ └─────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────────────┐ │
│ │ Prefetch Queue │ │
│ │ addr = current + Δ │ │
│ └─────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────┘
2.3 Design details¶
module neural_prefetcher #(
parameter HISTORY_DEPTH = 16, // Access history depth
parameter TABLE_SIZE = 64, // PC-indexed table size
parameter DELTA_BITS = 12, // Delta (address difference) bit width
parameter WEIGHT_BITS = 6, // Weight bit width
parameter NUM_DELTAS = 4 // Number of deltas to predict
)(
input logic clk_i,
input logic rst_ni,
// Cache access information
input logic access_valid_i,
input logic [XLEN-1:0] access_pc_i,
input logic [XLEN-1:0] access_addr_i,
input logic cache_hit_i,
// Prefetch outputs
output logic prefetch_valid_o,
output logic [XLEN-1:0] prefetch_addr_o,
output logic [1:0] prefetch_confidence_o
);
// Delta history per PC
typedef struct packed {
logic [DELTA_BITS-1:0] delta;
logic valid;
} delta_entry_t;
delta_entry_t delta_history [TABLE_SIZE][HISTORY_DEPTH];
logic [XLEN-1:0] last_addr [TABLE_SIZE];
// Perceptron weights: predict next delta based on delta history
logic signed [WEIGHT_BITS-1:0] weights [TABLE_SIZE][HISTORY_DEPTH];
logic signed [WEIGHT_BITS-1:0] bias [TABLE_SIZE];
// Delta pattern detection
logic [$clog2(TABLE_SIZE)-1:0] pc_idx;
logic signed [DELTA_BITS-1:0] current_delta;
logic signed [WEIGHT_BITS+$clog2(HISTORY_DEPTH):0] prediction_sum;
assign pc_idx = access_pc_i[$clog2(TABLE_SIZE)+1:2];
assign current_delta = access_addr_i - last_addr[pc_idx];
// Stride detection + neural prediction hybrid
always_comb begin
prediction_sum = bias[pc_idx];
for (int i = 0; i < HISTORY_DEPTH; i++) begin
if (delta_history[pc_idx][i].valid) begin
// Feature: delta match contributes positively
if (delta_history[pc_idx][i].delta == current_delta)
prediction_sum = prediction_sum + weights[pc_idx][i];
else
prediction_sum = prediction_sum - (weights[pc_idx][i] >>> 1);
end
end
// Prefetch decision
prefetch_valid_o = (prediction_sum > THRESHOLD) && access_valid_i;
prefetch_addr_o = access_addr_i + {{(XLEN-DELTA_BITS){current_delta[DELTA_BITS-1]}}, current_delta};
// Confidence based on prediction strength
if (prediction_sum > HIGH_THRESHOLD)
prefetch_confidence_o = 2'b11;
else if (prediction_sum > MED_THRESHOLD)
prefetch_confidence_o = 2'b10;
else if (prediction_sum > LOW_THRESHOLD)
prefetch_confidence_o = 2'b01;
else
prefetch_confidence_o = 2'b00;
end
// Training on cache access
always_ff @(posedge clk_i) begin
if (!rst_ni) begin
// Reset
end else if (access_valid_i) begin
// Update history
for (int i = HISTORY_DEPTH-1; i > 0; i--)
delta_history[pc_idx][i] <= delta_history[pc_idx][i-1];
delta_history[pc_idx][0] <= '{delta: current_delta, valid: 1'b1};
last_addr[pc_idx] <= access_addr_i;
// Update weights based on hit/miss
if (cache_hit_i) begin
// Reward pattern that led to hit
// (prefetch was useful)
end else begin
// Penalize - should have prefetched
end
end
end
endmodule
2.4 Integration points¶
- Add a prefetch interface to the
cache.svmodule - Coordinate prefetch queue with
memory.sv - Prefetch buffer (separate small buffer or cache way)
2.5 Expected benefit¶
- Cache hit rate: 10–25% increase
- Memory latency: ~15–30% average reduction
- Area cost: ~1KB (64 entries × 16 history × 6-bit weight)
3. Learned Cache Replacement Policy¶
3.1 Current state¶
- Algorithm: PLRU (pseudo least recently used)
- File:
rtl/core/mmu/cache.sv - Functions:
update_node(),compute_evict_way()
3.2 Proposed improvement: Hawkeye-inspired learned replacement¶
┌─────────────────────────────────────────────────────────────────┐
│ Learned Cache Replacement Policy │
├─────────────────────────────────────────────────────────────────┤
│ │
│ ┌─────────────────────┐ ┌─────────────────────────────┐ │
│ │ OPT Simulator │ │ Predictor (Perceptron) │ │
│ │ (Offline/Shadow) │ │ │ │
│ │ │ │ PC ──► ┌─────────┐ │ │
│ │ Belvedere's OPT │────►│ │ Weights │ ──► Prediction
│ │ approximation │ │ Type ─►└─────────┘ │ │
│ │ │ │ │ │
│ └─────────────────────┘ └─────────────────────────────┘ │
│ │ │ │
│ │ Training │ Eviction │
│ │ Labels │ Decision │
│ ▼ ▼ │
│ ┌─────────────────────────────────────────────────────────┐ │
│ │ Cache Controller │ │
│ │ • cache-friendly → high priority (keep) │ │
│ │ • cache-averse → low priority (evict first) │ │
│ └─────────────────────────────────────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────┘
3.3 Design details¶
module learned_replacement #(
parameter NUM_WAY = 4,
parameter NUM_SET = 64,
parameter PC_BITS = 12,
parameter WEIGHT_BITS = 4
)(
input logic clk_i,
input logic rst_ni,
// Access info
input logic access_valid_i,
input logic [PC_BITS-1:0] access_pc_i,
input logic [$clog2(NUM_SET)-1:0] set_idx_i,
input logic [NUM_WAY-1:0] hit_way_i,
input logic is_load_i,
// Eviction decision
output logic [NUM_WAY-1:0] evict_way_o,
output logic [NUM_WAY-1:0] priority_o // Per-way priority
);
// RRIP-style priority counters per cache line
logic [2:0] rrpv [NUM_SET][NUM_WAY]; // Re-Reference Prediction Value
// PC-indexed predictor: is this PC cache-friendly?
logic signed [WEIGHT_BITS-1:0] pc_weights [2**PC_BITS];
// Prediction
logic cache_friendly;
assign cache_friendly = (pc_weights[access_pc_i] >= 0);
// Update RRPV on access
always_ff @(posedge clk_i) begin
if (!rst_ni) begin
// Initialize all RRPV to distant (7)
for (int s = 0; s < NUM_SET; s++)
for (int w = 0; w < NUM_WAY; w++)
rrpv[s][w] <= 3'd7;
end else if (access_valid_i) begin
if (|hit_way_i) begin
// Hit: set RRPV based on prediction
for (int w = 0; w < NUM_WAY; w++) begin
if (hit_way_i[w]) begin
if (cache_friendly)
rrpv[set_idx_i][w] <= 3'd0; // Near re-reference
else
rrpv[set_idx_i][w] <= 3'd2; // Intermediate
end
end
end
end
end
// Eviction: choose way with highest RRPV
always_comb begin
evict_way_o = '0;
priority_o = '0;
// Find way with max RRPV (least likely to be reused)
logic [2:0] max_rrpv = 3'd0;
for (int w = 0; w < NUM_WAY; w++) begin
priority_o[w] = rrpv[set_idx_i][w];
if (rrpv[set_idx_i][w] >= max_rrpv) begin
max_rrpv = rrpv[set_idx_i][w];
evict_way_o = (1 << w);
end
end
end
// Training: update PC weights based on hit/miss
always_ff @(posedge clk_i) begin
if (access_valid_i) begin
if (|hit_way_i) begin
// Hit: this PC is cache-friendly
if (pc_weights[access_pc_i] < MAX_WEIGHT)
pc_weights[access_pc_i] <= pc_weights[access_pc_i] + 1;
end else begin
// Miss: this PC might be cache-averse
if (pc_weights[access_pc_i] > MIN_WEIGHT)
pc_weights[access_pc_i] <= pc_weights[access_pc_i] - 1;
end
end
end
endmodule
3.4 Integration plan¶
- Change
compute_evict_way()incache.sv - Use learned replacement instead of PLRU
- Hybrid mode: fall back to PLRU when confidence is low
3.5 Expected benefit¶
- Hit rate: 5–12% increase (workload-dependent)
- Streaming access: Large improvement (more scan-resistant)
4. Load/Store Stride Predictor¶
4.1 Current state¶
- File:
rtl/core/stage04_memory/memory.sv - Stride detection: None
4.2 Proposed improvement: neural stride predictor¶
┌─────────────────────────────────────────────────────────────────┐
│ Neural Stride Predictor │
├─────────────────────────────────────────────────────────────────┤
│ │
│ Load/Store PC ──────┬──────────────────────────────────────┐ │
│ │ │ │
│ ▼ │ │
│ ┌─────────────────────┐ │ │
│ │ Stride History │ │ │
│ │ Table (PC-indexed) │ │ │
│ │ ┌───────────────┐ │ │ │
│ │ │Last Addr │ │ │ │
│ │ │Stride │ │ │ │
│ │ │Confidence │ │ │ │
│ │ │State (train/ │ │ │ │
│ │ │ steady/no) │ │ │ │
│ │ └───────────────┘ │ │ │
│ └─────────────────────┘ │ │
│ │ │ │
│ ▼ │ │
│ ┌─────────────────────┐ │ │
│ │ Perceptron Layer │ │ │
│ │ (stride pattern │ │ │
│ │ prediction) │ │ │
│ └─────────────────────┘ │ │
│ │ │ │
│ ▼ │ │
│ ┌─────────────────────┐ ┌──────────────────┐ │ │
│ │ Predicted Next Addr │────►│ Prefetch Request │ │ │
│ └─────────────────────┘ └──────────────────┘ │ │
│ │
└─────────────────────────────────────────────────────────────────┘
4.3 Design details¶
module stride_predictor #(
parameter TABLE_SIZE = 64,
parameter STRIDE_BITS = 16,
parameter CONF_BITS = 3
)(
input logic clk_i,
input logic rst_ni,
// Memory access
input logic mem_valid_i,
input logic [XLEN-1:0] mem_pc_i,
input logic [XLEN-1:0] mem_addr_i,
input logic is_load_i,
// Prediction output
output logic predict_valid_o,
output logic [XLEN-1:0] predict_addr_o,
output logic [CONF_BITS-1:0] confidence_o
);
typedef enum logic [1:0] {
INIT, // First access
TRAINING, // Learning stride
STEADY, // Stable stride, predicting
NO_STRIDE // Irregular pattern
} state_e;
typedef struct packed {
logic [XLEN-1:0] last_addr;
logic signed [STRIDE_BITS-1:0] stride;
logic [CONF_BITS-1:0] confidence;
state_e state;
} stride_entry_t;
stride_entry_t table [TABLE_SIZE];
logic [$clog2(TABLE_SIZE)-1:0] idx;
logic signed [STRIDE_BITS-1:0] current_stride;
assign idx = mem_pc_i[$clog2(TABLE_SIZE)+1:2];
assign current_stride = mem_addr_i - table[idx].last_addr;
// Prediction logic
always_comb begin
predict_valid_o = 1'b0;
predict_addr_o = '0;
confidence_o = '0;
if (table[idx].state == STEADY && table[idx].confidence > 4) begin
predict_valid_o = 1'b1;
predict_addr_o = mem_addr_i + {{(XLEN-STRIDE_BITS){table[idx].stride[STRIDE_BITS-1]}},
table[idx].stride};
confidence_o = table[idx].confidence;
end
end
// Training FSM
always_ff @(posedge clk_i) begin
if (!rst_ni) begin
for (int i = 0; i < TABLE_SIZE; i++) begin
table[i] <= '{default: '0, state: INIT};
end
end else if (mem_valid_i && is_load_i) begin
case (table[idx].state)
INIT: begin
table[idx].last_addr <= mem_addr_i;
table[idx].state <= TRAINING;
end
TRAINING: begin
table[idx].stride <= current_stride;
table[idx].last_addr <= mem_addr_i;
table[idx].confidence <= 1;
table[idx].state <= STEADY;
end
STEADY: begin
table[idx].last_addr <= mem_addr_i;
if (current_stride == table[idx].stride) begin
// Stride matches, increase confidence
if (table[idx].confidence < MAX_CONF)
table[idx].confidence <= table[idx].confidence + 1;
end else begin
// Stride mismatch
if (table[idx].confidence > 0)
table[idx].confidence <= table[idx].confidence - 1;
else begin
table[idx].stride <= current_stride;
table[idx].state <= TRAINING;
end
end
end
NO_STRIDE: begin
// Periodically retry
table[idx].state <= INIT;
end
endcase
end
end
endmodule
4.4 Expected benefit¶
- Array traversal: Very high hit rate
- Linked list: Low (pointer chasing)
- Matrix ops: 80%+ prefetch accuracy with stride patterns
5. Hazard Prediction Unit¶
5.1 Current state¶
- File:
rtl/core/hazard_unit.sv - Current behavior: Reactive forwarding and stalls
5.2 Proposed improvement: predictive hazard detection¶
┌─────────────────────────────────────────────────────────────────┐
│ Predictive Hazard Unit │
├─────────────────────────────────────────────────────────────────┤
│ │
│ ┌─────────────────┐ │
│ │ Instruction │ │
│ │ Sequence │ │
│ │ History │ │
│ └────────┬────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────────────────────────────────────────────┐ │
│ │ Pattern Matching / Neural Network │ │
│ │ │ │
│ │ Sequence ──► [Hazard Pattern DB] ──► Prediction │ │
│ │ │ │
│ │ Features: │ │
│ │ • Opcode sequence │ │
│ │ • Register dependency graph │ │
│ │ • Memory access pattern │ │
│ │ │ │
│ └─────────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────────────────────────────────────────────┐ │
│ │ Proactive Actions: │ │
│ │ • Early stall insertion │ │
│ │ • Speculative forwarding path activation │ │
│ │ • Instruction reordering hints │ │
│ └─────────────────────────────────────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────┘
5.3 Use cases¶
- Load-use hazard prediction
- Long-latency operation detection (div/mul)
- Memory stall prediction
6. Workload-Aware Power Management¶
6.1 Current state¶
- No dynamic power management
6.2 Proposed improvement: neural DVFS controller¶
┌─────────────────────────────────────────────────────────────────┐
│ Neural Power Management Unit │
├─────────────────────────────────────────────────────────────────┤
│ │
│ ┌─────────────────────────────────────────────────────────┐ │
│ │ Activity Monitors │ │
│ │ • IPC (Instructions Per Cycle) │ │
│ │ • Cache miss rate │ │
│ │ • Branch misprediction rate │ │
│ │ • Memory bandwidth utilization │ │
│ │ • Stall cycles │ │
│ └────────────────────────┬────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────────────────────────────────────────────┐ │
│ │ Neural Network Predictor │ │
│ │ │ │
│ │ Input: Activity metrics (sliding window) │ │
│ │ Output: Predicted workload phase │ │
│ │ │ │
│ │ Phases: │ │
│ │ • Compute-intensive (high freq needed) │ │
│ │ • Memory-bound (can reduce freq) │ │
│ │ • Idle (aggressive power saving) │ │
│ │ • Mixed │ │
│ └────────────────────────┬────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────────────────────────────────────────────┐ │
│ │ DVFS Controller │ │
│ │ │ │
│ │ • Frequency scaling │ │
│ │ • Voltage adjustment │ │
│ │ • Clock gating decisions │ │
│ └─────────────────────────────────────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────┘
6.3 Design details¶
module neural_power_manager #(
parameter WINDOW_SIZE = 1024, // Sampling window
parameter NUM_FEATURES = 8,
parameter HIDDEN_SIZE = 16
)(
input logic clk_i,
input logic rst_ni,
// Performance counters
input logic [31:0] cycle_count_i,
input logic [31:0] inst_count_i,
input logic [31:0] cache_miss_i,
input logic [31:0] branch_miss_i,
input logic [31:0] stall_cycles_i,
// Power control outputs
output logic [2:0] freq_level_o, // 0=lowest, 7=highest
output logic clock_gate_o, // Gate unused units
output logic [3:0] active_units_o // Which units to keep active
);
// Feature extraction
logic [15:0] ipc; // Instructions per cycle (fixed point)
logic [15:0] miss_rate; // Cache miss rate
logic [15:0] stall_rate; // Stall percentage
// Simple perceptron for phase detection
typedef enum logic [1:0] {
PHASE_COMPUTE,
PHASE_MEMORY,
PHASE_IDLE,
PHASE_MIXED
} phase_e;
phase_e current_phase;
// Phase detection logic
always_ff @(posedge clk_i) begin
if (!rst_ni) begin
current_phase <= PHASE_MIXED;
freq_level_o <= 3'd4; // Medium frequency
end else begin
// Simple heuristic-based phase detection
// Can be replaced with trained neural network
if (ipc > HIGH_IPC_THRESHOLD && miss_rate < LOW_MISS_THRESHOLD) begin
current_phase <= PHASE_COMPUTE;
freq_level_o <= 3'd7; // Max frequency
end else if (miss_rate > HIGH_MISS_THRESHOLD) begin
current_phase <= PHASE_MEMORY;
freq_level_o <= 3'd3; // Lower frequency (memory bound)
end else if (stall_rate > IDLE_THRESHOLD) begin
current_phase <= PHASE_IDLE;
freq_level_o <= 3'd1; // Minimum frequency
clock_gate_o <= 1'b1;
end else begin
current_phase <= PHASE_MIXED;
freq_level_o <= 3'd4;
end
end
end
endmodule
📊 Comparison table¶
| Feature | Area cost | Design complexity | Expected benefit | Priority |
|---|---|---|---|---|
| Neural branch predictor | ~2KB | Medium | 5–15% misprediction↓ | ⭐⭐⭐⭐ |
| Neural cache prefetcher | ~1KB | Medium | 10–25% hit rate↑ | ⭐⭐⭐⭐⭐ |
| Learned replacement | ~0.5KB | Low | 5–12% hit rate↑ | ⭐⭐⭐ |
| Stride predictor | ~0.5KB | Low | High for array workloads | ⭐⭐⭐ |
| Hazard prediction | ~0.25KB | Medium | 5–10% stall↓ | ⭐⭐ |
| Power management | ~0.5KB | High | 20–40% power↓ | ⭐⭐⭐ |
🚀 Implementation roadmap¶
Phase 1: Core infrastructure (1–2 weeks)¶
- Add performance counters (IPC, cache miss, branch miss)
- Mechanism to collect training data
Phase 2: First AI module (2–3 weeks)¶
- Neural cache prefetcher implementation
- Hybrid approach with stride predictor
- Benchmark tests
Phase 3: Branch predictor upgrade (2–3 weeks)¶
- Perceptron predictor module
- Tournament predictor integration
- A/B test infrastructure
Phase 4: Advanced features (3–4 weeks)¶
- Learned cache replacement
- Power management
- Full system integration
📚 References¶
- Perceptron Branch Predictor: Jiménez & Lin, "Dynamic Branch Prediction with Perceptrons", HPCA 2001
- Hawkeye Cache: Jain & Lin, "Back to the Future: Leveraging Belady's Algorithm for Improved Cache Replacement", ISCA 2016
- Neural Prefetching: Hashemi et al., "Learning Memory Access Patterns", ICML 2018
- RRIP Replacement: Jaleel et al., "High Performance Cache Replacement Using Re-Reference Interval Prediction", ISCA 2010
This document contains AI/ML improvement plans for the Level RISC-V processor.
Last updated: December 2025